Mine car driving system optimization control method based on SMA-PSO

Abstract

The invention discloses a mine car driving system optimization control method based on SMA-PSO, and the method comprises the steps: obtaining the angle of an accelerator pedal in a current control period k, the angle of a deceleration pedal in the current control period k, and the maximum angular acceleration of a left driving wheel and a right driving wheel of a mine car driving system; the angle of the accelerator pedal in the current control period k and the angle of the deceleration pedal in the current control period k are preprocessed, and reference angular velocities of the left driving wheel and the right driving wheel in the next control period k + 1 are obtained; and acquiring a comprehensive optimization target mathematical model of the mine car driving system in the acceleration stage and the deceleration stage in the current control period k, and solving the comprehensive optimization target mathematical model of the mine car driving system in the acceleration stage and the deceleration stage in the current control period k through a mucous membrane algorithm (SMA). The technical problems that in an existing discrete logic threshold control strategy, the controlled quantity is discontinuous, fluctuation is large, and the control effect is inaccurate can be solved.

Description

Technical Field

[0001] The present invention belongs to the field of optimization control of drive systems of electric vehicles, and more specifically, relates to an optimization control method for a mine car drive system based on a slime mold algorithm (Slime Mould Algorithm, referred to as SMA)-particle swarm optimization (Particle Swarm Optimization, referred to as PSO). Background Art

[0002] The main task of the mine car's drive system is to control the drive torque of the mine car's inverter at the current moment by collecting the opening of the acceleration and deceleration pedals. At the same time, it is also necessary to control the generator to ensure that the generator can provide a stable and reliable energy supply to the inverter. The mine car's drive system mainly controls the drive torque and energy of the mine car based on the acceleration and deceleration pedals, but how to ensure the stable operation of the drive system while ensuring the lowest maintenance cost is an important issue.

[0003] The existing drive system control strategies mainly include discrete logic threshold control strategy, control strategy with vehicle slip rate as control target, control strategy combining braking feedback and mechanical braking, and energy allocation strategy with minimum energy consumption; the discrete logic threshold control strategy sets several thresholds for each controlled quantity, and selects different control schemes according to the threshold range of the current operating state of the vehicle; the control strategy with vehicle slip rate as control target is to control the slip rate of the left drive wheel and the right drive wheel near the optimal slip rate and set it as the control target to control the driving torque of the left drive wheel and the right drive wheel; the control strategy combining braking feedback and mechanical braking is to distribute the braking torque to the braking feedback system and the mechanical brake system in the optimal ratio to achieve vehicle deceleration; the energy allocation strategy with minimum energy consumption is to control the optimal output power of the generator and the power battery by considering the engine fuel consumption and the power battery loss.

[0004] However, the above-mentioned several drive system control strategies mainly have the following problems:

[0005] (1) The discrete logic threshold control strategy controls each controlled variable separately without considering the coupling between the controlled variables. At the same time, the control strategies between different thresholds are not continuous, resulting in large fluctuations in the controlled variables.

[0006] (2) The control strategy that takes vehicle slip rate as the control target only considers the driving torque control but ignores the energy distribution of the vehicle, resulting in high maintenance costs for the energy supply system.

[0007] (3) Since the mine car uses a combination of braking resistance and braking feedback for braking, and the control strategy combining braking feedback and mechanical braking does not control the braking resistance, this control strategy cannot be applied to the mine car;

[0008] (4) The energy distribution strategy with the lowest energy consumption only considers the energy distribution problem of the energy supply system, but does not consider the change in energy consumption caused by the drive torque control, which will lead to large fluctuations in the DC bus voltage. Summary of the invention

[0009] In view of the above defects or improvement needs of the prior art, the present invention provides a method for optimizing the control of a mine car drive system based on SMA-PSO. The purpose is to solve the technical problems that the existing discrete logic threshold control strategy adopts each controlled quantity separately without considering the coupling between the controlled quantities, and the control strategies between different thresholds are not continuous, resulting in discontinuous and large fluctuations in the controlled quantity and inaccurate control effect, and the existing control strategy with vehicle slip rate as the control target only considers the drive torque control without considering the energy distribution of the vehicle, resulting in high maintenance cost of the energy supply system, and the existing control strategy combining braking feedback and mechanical braking cannot be applied to the mine car because the mine car uses a combination of braking resistor and braking feedback for braking, and the existing energy distribution strategy with the lowest energy consumption only considers the energy distribution problem of the energy supply system without considering the change of energy consumption caused by the drive torque control, thus resulting in a large fluctuation of the DC bus voltage.

[0010] To achieve the above object, according to one aspect of the present invention, a method for optimizing and controlling a mine car driving system based on SMA-PSO is provided, comprising the following steps:

[0011] (1) Obtain the angle of the accelerator pedal in the current control cycle k, the angle of the decelerator pedal in the current control cycle k, and the maximum angular acceleration of the left drive wheel and the right drive wheel of the mine car's drive system, pre-process the angle of the accelerator pedal in the current control cycle k and the angle of the decelerator pedal in the current control cycle k, and obtain the reference angular velocity of the left drive wheel and the right drive wheel in the next control cycle k+1 according to the opening degree of the accelerator pedal after processing, the opening degree of the decelerator pedal after processing, the maximum angular acceleration of the left drive wheel and the right drive wheel of the mine car's drive system, and the reference angular velocity of the left drive wheel and the right drive wheel in the current control cycle k.

[0012] (2) According to the reference angular velocities of the left driving wheel and the right driving wheel in the next control cycle k+1 obtained in step (1), a comprehensive optimization target mathematical model of the mine car drive system in the acceleration phase and the deceleration phase in the current control cycle k is obtained.

[0013] (3) The comprehensive optimization target mathematical model of the mine car drive system in the acceleration stage and the deceleration stage obtained in step (2) is solved by the slime mold algorithm SMA to obtain the optimal solution.

[0014] (4) Based on the possible optimal solution obtained in step (3), the improved PSO algorithm is used to obtain the final optimal solution, and the solution is used as the output signal of the drive system to control the mine car drive system.

[0015] Preferably, step (1) comprises the following sub-steps:

[0016] (1-1) Get the angle of the accelerator pedal in the current control cycle k and the angle of the deceleration pedal in the current control cycle k The angle of the accelerator pedal in the current control cycle k and the angle of the deceleration pedal in the current control cycle k are normalized to obtain the normalized opening of the accelerator pedal in the current control cycle k. and the normalized opening of the deceleration pedal in the current control cycle k

[0017]

[0018] in is the normalized opening of the accelerator pedal in the current control cycle k; is the angle of the accelerator pedal in the current control cycle k; θ accmax is the maximum angle of the accelerator pedal; θ accmin is the minimum angle of the accelerator pedal; is the normalized opening degree of the deceleration pedal in the current control cycle k; is the angle of the deceleration pedal in the current control cycle k; θ retmax is the maximum angle of the deceleration pedal; θ retmin is the minimum angle of the deceleration pedal; k is the current control cycle.

[0019] (1-2) Determine whether the normalized opening of the deceleration pedal obtained in step (1-1) is greater than 0. If so, proceed to step (1-4); otherwise, proceed to step (1-3);

[0020] (1-3) Determine whether the normalized opening of the accelerator pedal obtained in step (1-1) is greater than 0, if so, proceed to step (1-5), otherwise proceed to step (1-6);

[0021] (1-4) The total pedal opening value in the current control cycle k Set to the normalized opening of the deceleration pedal

[0022] Then go to step (1-7);

[0023] (1-5) The total pedal opening value in the current control cycle k Set to the normalized opening of the accelerator pedal

[0024] Then go to step (1-7);

[0025] (1-6) The total pedal opening value in the current control cycle k Set to 0, then go to step (1-7);

[0026] (1-7) Based on the value of the total pedal opening in the current control cycle k Maximum angular acceleration of the left and right drive wheels The reference angular velocity of the left and right driving wheels in the current control cycle k Get the reference angular velocity of the left and right drive wheels in the next control cycle k+1

[0027]

[0028] Preferably, step (2) comprises the following sub-steps:

[0029] (2-1) Determine whether the value of the total pedal opening in the current control cycle k is less than or equal to 0. If so, proceed to step (2-2). Otherwise, obtain the comprehensive optimization target mathematical model of the mine car drive system in the acceleration stage in the current control cycle k according to the reference angular velocity of the left drive wheel and the right drive wheel obtained in step (1-7) in the next control cycle k+1. Then the process ends.

[0030] (2-2) Obtain the comprehensive optimization target mathematical model of the mine car drive system in the deceleration stage in the current control cycle k according to the reference angular velocities of the left drive wheel and the right drive wheel in the next control cycle k+1 obtained in step (1-7) Then the process ends.

[0031] Preferably, step (2-1) comprises the following sub-steps:

[0032] (2-1-1) Obtain the operating parameters of the mine car drive system in the acceleration stage, and calculate the penalty value and loss value corresponding to the difference between the angular velocity of the left drive wheel and the right drive wheel in the next control cycle k+1 and the reference angular velocity according to the operating parameters and the reference angular velocity of the left drive wheel and the right drive wheel in the next control cycle k+1 obtained in step (1-7), and obtain the preliminary optimization objective function F of the mine car drive system in the acceleration stage according to the obtained penalty value and loss value. 0acc The operating parameters of the mine car drive system in the acceleration stage include: the rotational inertia J of the left drive wheel and the right drive wheel of the mine car drive system, the control cycle duration t of the mine car drive system, the angular velocity of the left drive wheel and the right drive wheel in the current control cycle k and The driving torque of the left and right driving wheels in the current control cycle k and The load torque of the left and right driving wheels in the current control cycle k and The angular velocity update values ​​of the left and right drive wheels in the next control cycle k+1 and Reference angular velocity difference penalty coefficient C1, slip angular velocity difference penalty coefficient C2, fuel price C3 of the engine used in the mine car drive system, and speed n corresponding to the minimum fuel consumption of the engine min , the charge and discharge loss coefficient C4 of the power battery in the mine car drive system, the speed n of the engine in the current control cycle k k , and the charge and discharge power of the power battery in the current control cycle k

[0033] (2-1-2) According to the preliminary optimization objective function of the mine car drive system in the acceleration stage in the current control cycle k obtained in step (2-1-1), the inequality constraint relationship and equality constraint relationship of the controlled quantity in the preliminary optimization objective function are obtained.

[0034] (2-1-3) The equality constraint relationship and inequality constraint relationship of the controlled quantity in the preliminary optimization objective function of the mine car drive system in the acceleration stage obtained in step (2-1-2) in the current control cycle k are respectively converted to obtain the penalty function corresponding to the equality constraint relationship The barrier function B corresponding to the inequality constraint x .

[0035] (2-1-4) Sum the penalty function and barrier function obtained in step (2-1-3) and the preliminary optimization objective function of the mine car drive system in the acceleration phase in the current control cycle k obtained in step (2-1-1) to obtain a comprehensive optimization objective mathematical model of the mine car drive system in the acceleration phase in the current control cycle k

[0036] Preferably, step (2-1-1) is specifically as follows: first, according to the rotational inertia J of the left driving wheel and the right driving wheel, the control cycle duration t of the mine car driving system, and the angular velocity of the left driving wheel and the right driving wheel in the current control cycle k, and The driving torque of the left and right driving wheels in the current control cycle k and The load torque of the left and right driving wheels in the current control cycle k and Get the updated angular velocity values ​​of the left and right driving wheels of the mine car drive system in the acceleration phase in the next control cycle k+1 respectively and

[0037]

[0038] Then, according to the reference angular velocity difference penalty coefficient C1, the angular velocity of the left driving wheel and the right driving wheel in the next control cycle k+1 and And the reference angular velocity of the left driving wheel and the right driving wheel obtained in step (1-7) in the next control cycle k+1 Get the penalty value corresponding to the difference between the angular velocity of the left drive wheel and the right drive wheel in the next control cycle k+1 and the reference angular velocity

[0039]

[0040] The value range of C1 is [0,1], and the preferred value is 0.9.

[0041] Then, according to the angular velocity of the left driving wheel and the right driving wheel in the next control cycle k+1 and Get the penalty value corresponding to the difference in angular velocity between the left drive wheel and the right drive wheel in the next control cycle k+1

[0042]

[0043] The value range of C2 is [0,1], and the preferred value is 0.85.

[0044] Then, according to the fuel price of the engine, the fuel consumption of the engine in the current control cycle k The engine speed n in the current control cycle k k 、Minimum fuel consumption of the engine G emin And the engine speed n corresponding to the lowest fuel consumption of the engine min Get the loss value of the engine in the current control cycle k

[0045]

[0046] where a ei (i=1, 2, 3) is the fitting coefficient of engine fuel consumption and engine speed, which is obtained by inputting the speed and fuel consumption data of the engine in the mine car drive system into MATLAB software and performing data fitting.

[0047] Then, according to the power battery charge and discharge loss coefficient C4 and the charge and discharge power of the power battery in the current control cycle k Get the loss value of the power battery in the current control cycle k

[0048]

[0049] The value range of C4 is [0,1], and the preferred value is 0.6. When The value of represents the power battery discharge power; When The value represents the charging power of the power battery.

[0050] Finally, the reference angular velocity difference penalty value obtained above is The penalty value corresponding to the difference in slip angular velocity Engine loss value Power battery loss value The sum is calculated to obtain the preliminary optimization objective function of the mine car drive system in the acceleration phase in the current control cycle k.

[0051] Step (2-1-2) is specifically as follows: first, according to the upper and lower boundary values ​​of the controlled quantity in the preliminary optimization objective function of the mine car drive system in the acceleration stage, the inequality constraint relationship of the controlled quantity in the preliminary optimization objective function of the current control cycle k is determined, and the upper and lower boundary values ​​include: the maximum power consumption P of the left driving wheel and the right driving wheel motmax , Maximum engine speed n max , the maximum excitation current I of the generator of the mine car drive system max、The maximum output power of the generator P Gmax , and the maximum charge and discharge power P of the power battery Bmax ;

[0052] The inequality constraint relationship of the controlled quantity in the preliminary optimization objective function specifically includes the following inequality constraints:

[0053] A. Inequality constraints on the power consumption of the left and right drive wheels in the current control cycle k:

[0054]

[0055] in and are the power consumption of the left and right driving wheels respectively; P motmax is the maximum power consumption of the left and right drive wheels,

[0056] B. Inequality constraint relationship of the engine speed in the current control cycle k:

[0057] 0≤n k ≤n max

[0058] C. The inequality constraint relationship between the excitation current of the generator in the current control cycle k and the output power of the generator in the current control cycle k:

[0059] 0≤I k ≤I max

[0060]

[0061] Among them I k is the excitation current of the generator in the current control cycle k, is the output power of the generator in the current control cycle k, I max and P Gmax is the maximum excitation current of the generator and the maximum output power of the generator.

[0062] D. The inequality constraint relationship of the charge and discharge power of the power battery in the current control cycle k:

[0063]

[0064] Where P Bmax It is the maximum charge and discharge power of the power battery.

[0065] Then, according to the intrinsic coupling relationship of the controlled quantity of the mine car drive system, the equation constraint relationship of the controlled quantity in the preliminary optimization objective function of the mine car drive system in the acceleration stage is obtained, which is expressed by the following formula:

[0066]

[0067] Step (2-1-3) is as follows: first, the corresponding penalty function is obtained according to the equality constraint relationship.

[0068]

[0069] Where C5 is the equality constraint penalty coefficient, which ranges from [0,1] and is preferably 0.4; a Gj (j=1,2) is the fitting value of generator output power and engine speed; a Gj (j=3, 4, 5) is the fitting value of the generator output power and the generator excitation current. The coefficient is obtained by inputting the engine speed, generator excitation current and generator output power data of the mine car drive system into MATLAB software for data fitting.

[0070] Then, the inequality constraint relationship is transformed into a logarithmic barrier to obtain the corresponding barrier function B x :

[0071] B x = -C x lg(x max ―x)

[0072] The value of x is n k ,I k ,as well as x max express n k ,I k ,as well as The maximum value in C x is the weight coefficient, and its value range is [0,1], C x The smaller it is, the closer the optimization result of x is to x. max ; and Preferably 0.3; Preferably 0.2; Preferably 0.1; Preferably 0.4;

[0073] Then, the inequality constraint relationship is transformed into a quadratic logarithmic barrier to obtain the corresponding barrier function

[0074]

[0075] The value of y is Ct is the weight coefficient, the range is [0,1], and 0.1 is preferred. t The smaller it is, the closer the optimization result of y is to y max . Preferably it is 0.2.

[0076] The comprehensive optimization target mathematical model of the mine car drive system in the acceleration phase in the current control cycle k in step (2-1-4) It is expressed by the following formula:

[0077]

[0078]

[0079] Where x is the controlled variable,

[0080] Preferably, step (2-2) specifically includes the following sub-steps:

[0081] (2-2-1) Obtain the operating parameters of the mine car drive system in the deceleration stage, and calculate the penalty value and loss value corresponding to the difference between the angular velocity of the left drive wheel and the right drive wheel in the next control cycle k+1 and the reference angular velocity according to the operating parameters and the reference angular velocity of the left drive wheel and the right drive wheel in the next control cycle k+1 obtained in step (1-7), and obtain the preliminary optimization objective function F of the mine car drive system in the deceleration stage according to the obtained penalty value and loss value. 0ret , where the operating parameters of the mine car drive system in the deceleration stage include: the rotational inertia J of the left and right drive wheels of the mine car drive system, the control cycle duration t of the mine car drive system, the input power of the brake resistor in the current control cycle k The angular velocity of the left and right driving wheels in the current control cycle k and The driving torque of the left and right driving wheels in the current control cycle k and The load torque of the left and right driving wheels in the current control cycle k and The angular velocity update values ​​of the left and right drive wheels in the next control cycle k+1 and Reference angular velocity difference penalty coefficient C1, slip angular velocity difference penalty coefficient C2, fuel price C3 of the engine used in the mine car drive system, and speed n corresponding to the minimum fuel consumption of the engine min , the charge and discharge loss coefficient C4 of the power battery in the mine car drive system, the speed n of the engine in the current control cycle k k , and the charge and discharge power of the power battery in the current control cycle k

[0082] (2-2-2) According to the preliminary optimization objective function of the mine car drive system in the deceleration stage in the current control cycle k obtained in step (2-2-1), the obstacle function corresponding to the inequality constraint relationship of the controlled quantity in the preliminary optimization objective function and the penalty function corresponding to the equality constraint relationship are obtained.

[0083] (2-2-3) Transform the inequality constraint relationship and equality constraint relationship of the controlled quantity in the preliminary optimization objective function of the mine car drive system in the deceleration stage obtained in step (2-2-2) in the current control cycle k to obtain the penalty function corresponding to the equality constraint relationship The barrier function B corresponding to the inequality constraint relationship x .

[0084] (2-2-4) Sum the penalty function and barrier function obtained in step (2-2-3) and the preliminary optimization objective function of the mine car drive system in the deceleration stage in the current control cycle k obtained in step (2-2-1) to obtain a comprehensive optimization objective mathematical model of the mine car drive system in the deceleration stage in the current control cycle k

[0085] Preferably, this step (2-2-1) is specifically as follows: first, according to the rotational inertia J of the left driving wheel and the right driving wheel, the control cycle duration t of the mine car driving system, and the load torque of the left driving wheel and the right driving wheel in the current control cycle k, and The angular velocity of the left and right driving wheels in the current control cycle k and The driving torque of the left and right driving wheels in the current control cycle k and The input power of the left brake resistor and the right brake resistor in the current control cycle k and Get the updated angular velocity values ​​of the left and right driving wheels of the mine car drive system in the deceleration phase in the next control cycle k+1 respectively and

[0086]

[0087] Then, the penalty value of the reference angular velocity difference in the current control cycle k is obtained according to the formula in step (2-1-1): Penalty value of slip angular velocity difference in the current control cycle k The loss value of the engine in the current control cycle k And the loss value of the power battery in the current control cycle k

[0088] Then, the reference angular velocity difference obtained in the above step is calculated as the penalty value of the current control cycle k. Penalty value of slip angular velocity difference in the current control cycle k The loss value of the engine in the current control cycle k And the loss value of the power battery in the current control cycle k Sum to obtain the preliminary optimization objective function of the mine car drive system in the deceleration stage in the current control cycle k

[0089] Step (2-2-2) is specifically as follows: first, according to the upper and lower boundary values ​​of the controlled quantity in the preliminary optimization objective function of the mine car drive system in the deceleration stage in the current control cycle k, the inequality constraint relationship of the controlled quantity in the preliminary optimization objective function is determined, and the upper and lower boundary values ​​include: the maximum power consumption P of the left driving wheel and the right driving wheel motmax , the maximum excitation current of the generator I max 、The maximum output power of the generator P Gmax , the maximum recovery power P of the kinetic energy recovery system regmax 、Maximum charge and discharge power of power battery P Bmax , Maximum input power P of the brake resistor Rmax :

[0090] The inequality constraint relationship of the controlled quantity in the preliminary optimization objective function specifically includes the following inequality constraints:

[0091] A. Inequality constraints on the kinetic energy recovery power of the left and right driving wheels in the current control cycle k:

[0092]

[0093] in and is the kinetic energy recovery power of the left driving wheel and the right driving wheel in the current control cycle k;

[0094] P regmax It is the maximum kinetic energy recovery power of the left drive wheel and the right drive wheel. τ is the electromechanical conversion efficiency of the left drive wheel and the right drive wheel.

[0095] B. Inequality constraint relationship of the engine speed in the current control cycle k:

[0096] 0≤n k ≤n max

[0097] C. The inequality constraint relationship between the excitation current of the generator in the current control cycle k and the output power of the generator in the current control cycle k:

[0098] 0≤I k ≤I max

[0099]

[0100] D. The inequality constraint relationship of the charge and discharge power of the power battery in the current control cycle k:

[0101]

[0102] E. The input power inequality constraint of the left brake resistor and the right brake resistor in the current control cycle k is expressed by the following formula:

[0103]

[0104] in and is the input power of the left brake resistor and the right brake resistor in the current control cycle k; P Rmax It is the maximum input power of the braking resistor.

[0105] Then, according to the intrinsic coupling relationship of the controlled quantity of the mine car drive system, the equation constraint relationship of the controlled quantity in the preliminary optimization objective function of the mine car drive system in the deceleration stage in the current control cycle k is obtained:

[0106]

[0107] Step (2-2-3) is specifically as follows: first, according to the above equality constraint relationship, the penalty function corresponding to the equality constraint relationship can be obtained:

[0108]

[0109] Then, the inequality constraint relationship is transformed into a logarithmic barrier to obtain the corresponding barrier function B z :

[0110] B z = -C z lg(z max ―z)

[0111] Where z is n k ,I k , as well as C z is the weight coefficient, which ranges from [0,1], C x The smaller it is, the closer the optimization result of x is to x.max . and Preferably 0.3; and Preferably it is 0.6.

[0112] Then, in the same way as in step (2-1-3), the inequality constraint relationship is transformed into a quadratic logarithmic barrier to obtain the corresponding barrier function

[0113]

[0114] The comprehensive optimization target mathematical model of the mine car drive system in the deceleration stage in step (2-2-4) in the current control cycle k It is expressed by the following formula:

[0115]

[0116]

[0117] Where x is the controlled variable,

[0118] Preferably, step (3) comprises the following sub-steps:

[0119] (3-1) Initialize the parameters of the SMA algorithm, including: initializing the number of slime molds Pop = 20; initializing the random search probability z ra =0.2; initialize the maximum number of iterations C max =50; Initialize the lower boundary of the search space Bd low =0; Initialize the upper boundary of the search space where e l is the unit vector corresponding to each component in the search space; initialize the value of the controlled variable X = [T L0 T R0 n0P B0 P RR0 P RL0 ], where T L0 and T R0 are the initial values ​​of the left drive wheel torque and the right drive wheel torque respectively; n0 is the initial value of the engine speed; I0 is the initial value of the excitation current; P B0 is the initial value of the power battery output power; P RR0 The initial value of the power input to the right brake resistor; P RL0 is the initial value of the left brake resistor input power, and initializes the number of SMA iterations k SMA is 1; randomly generate the dth slime mold at the kth SMA The position in the search space at the iteration As the slime mold position corresponding to the slime mold, d∈[1,Pop]; generate a random number for the randomly generated d-th slime mold, the range of the random number is [0,1], as the random weight change rate r of the slime mold d .

[0120] (3-2) Determine the number of SMA iterations k in the parameters of the SMA algorithm initialized in step (3-1) SMA Is it greater than the maximum number of iterations C? max , if yes, go to step (3-9), otherwise go to step (3-3);

[0121] (3-3) According to the parameters of the SMA algorithm initialized in step (3-1) and the comprehensive optimization target mathematical model of the mine car drive system in the acceleration stage and the deceleration stage in the current control cycle k obtained in step (2), the kth position of each slime mold in the SMA algorithm is obtained. SMA The food concentration value at the iteration If the mine car drive system is in the deceleration stage, then:

[0122]

[0123] If it is in the acceleration phase:

[0124]

[0125] in is the d-th slime mold at the kth SMA The food concentration during the second iteration is; is the d-th slime mold at the kth SMA The position in the search space at the iteration, d∈[1,Pop].

[0126] (3-4) For all slime molds obtained in step (3-3), SMA The food concentration value at the iteration Sort by the kth SMA The highest food concentration value at the iteration and its corresponding slime mold position and the minimum food concentration

[0127] (3-5) For the dth slime mold in the SMA algorithm, the random change rate r of the weight of the slime mold obtained in step (3-1) is d , and the highest food concentration value and the lowest food concentration value obtained in step (3-4), to obtain the value of the slime mold at the kth SMA The slime mold weight at iteration

[0128]

[0129] in For all slime molds in the kth SMA The highest food concentration value at the iteration; For all slime molds in the kth SMA The lowest food concentration value at iteration .

[0130] (3-6) For the dth slime mold in the SMA algorithm, the highest food concentration value obtained in step (3-4) is used to obtain the kth slime mold. SMA The position update value

[0131]

[0132] in The slime mold is SMA The position update value of this time is the position update value.

[0133] (3-7) Randomly select two slime molds α and β from all slime molds in the SMA algorithm and obtain their corresponding positions as X α and X β For the dth slime mold in the SMA algorithm, the random weight change rate r of the slime mold obtained in step (3-1) is d , the upper boundary of the search space Bd up , the lower boundary of the search space Bd low , random search probability z ra , the slime mold position corresponding to the highest food concentration value obtained in step (3-4) The weight of slime mold obtained in step (3-5) The location update value obtained in step (3-6) Get the kth SMA +1 Slime Mold Position at Iteration

[0134]

[0135] where z ra is the random search probability, its value is 0.2; v g is the information interaction weight, which is a random number and its range is Obtained by a random number generator; v s Reserve the weight for itself, which is a random number in the range Obtained from a random number generator.

[0136] (3-8) Set k SMA =k SMA +1, and return to step (3-2).

[0137] (3-9) According to step (3-3), all slime molds obtained are k SMA The food concentration values ​​at the iteration are sorted from high to low, and the first Food concentration value and its corresponding slime mold position as the best possible solution.

[0138] Preferably, step (4) comprises the following sub-steps:

[0139] (4-1) Initializing the parameters of the particle swarm algorithm according to the possible optimal solution obtained in step (3-9) to obtain the initialized parameters, including: initializing the number of particle swarms N PSO =Pop; Initialize the lower boundary of each particle's velocity where e u is the unit vector corresponding to each component of the velocity of each particle; the upper boundary of the velocity of each particle is initialized as Initialize the lower boundary x of each particle's position low =0; Initialize the upper boundary of each particle's position Initialize the maximum number of iterations S max is 20; obtained in step (3-9) Each slime mold position in the particles, and initialize the speed of each particle to 0, and initialize the number of PSO iterations k PSO =1.

[0140] (4-2) Determine the number of PSO iterations k PSO Is it greater than the maximum number of iterations S? max If yes, go to step (4-9), otherwise go to step (4-3).

[0141] (4-3) According to the food concentration value of the dth slime mold in the SMA algorithm obtained in step (3-3), the kth particle in the PSO algorithm is obtained. PSO The fitness at the iteration If the mine car drive system is in the deceleration stage, then:

[0142]

[0143] If the minecart drive system is in the acceleration phase, then:

[0144]

[0145] where p∈[1,N PSO ].

[0146] (4-4) According to the PSO algorithm obtained in step (4-3), each particle in the kth PSO The fitness at the iteration Get the optimal fitness of the entire particle swarm and its corresponding position For all particles obtained in step (4-3), PSO As for the fitness at the iteration, the position of the particle corresponding to the lowest fitness is taken as the position corresponding to the optimal fitness of the entire particle swarm. And take the minimum fitness as the optimal fitness L of the entire particle swarm b .

[0147] (4-5) Determine the pth particle in the PSO algorithm at the kth PSO Is the fitness during iteration less than the kth PSO -1 The fitness at the time of iteration, if yes, then the kth PSO The iterated position is set to the pth particle at the kth PSO The position corresponding to the iterated individual optimal value Then go to step (4-6); otherwise, PSO The position at the -1 iteration is set to the position of the pth particle corresponding to the individual optimal value at the vth iteration. Then proceed to step (4-6).

[0148] (4-6) The fitness of each particle obtained according to step (4-3) And the optimal fitness of the entire particle swarm obtained in step (4-4) Get the pth particle at the kth PSO Compression factor during iteration The pth particle is in the kth PSO Individual learning factor during iteration and the pth particle is at the kth PSO Group learning factor during iteration

[0149] (4-7) According to the calculation in (4-6), the pth particle is in the kth PSO Individual learning factor during iteration The pth particle is in the kth PSO Group learning factor during iteration The pth particle is in the kth PSO Compression factor during iteration Get the pth particle at the kth PSO +1 position and velocity at iteration

[0150] (4-8) Set kPSO =k PSO +1, and return to step (4-2);

[0151] (4-9) The position corresponding to the optimal fitness of the entire particle swarm is obtained As the final optimal solution, it is used as the output signal of the drive system to control each subsystem of the mine car drive system.

[0152] Preferably, step (4-6) is specifically as follows: first, obtain the p-th particle at the k-th PSO Individual learning factor during iteration and the pth particle is at the kth PSO Group learning factor during iteration

[0153]

[0154] Then, the p-th particle in the k-th particle is calculated by the following formula PSO Compression factor during iteration

[0155]

[0156] in is the pth particle at the kth PSO The auxiliary factor during iteration is equal to:

[0157]

[0158] Step (4-7) is as follows: first, calculate the p-th particle at the k-th PSO +1 Iteration position and the pth particle is at the kth PSO +1 Iteration speed It is expressed by the following formula:

[0159]

[0160] Where r1 and r2 are random numbers, both in the range of [0,1], obtained by the random number generator;

[0161]

[0162] in is the pth particle at the kth PSO +1 Updated speed at iteration; is the pth particle at the kth PSO The speed of iteration; is the pth particle at the kth PSO The position when iterating.

[0163] Then, calculate the p-th particle at the k-th PSO +1 position at iteration

[0164]

[0165] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0166] (1) Since the present invention establishes an optimization target mathematical model of the mine car driving system in steps (1) and (2), and directly converts the constraint relationship into an obstacle function and adds it into the optimization target mathematical model, the fluctuation range of the controlled quantity is limited within the threshold value each time the mine car is controlled, thereby reducing the fluctuation range of the controlled quantity;

[0167] (2) Since the present invention adopts step (2-1) and step (2-2), a loss and penalty mathematical model of the mine car drive system in the acceleration stage in the current control cycle k and a loss and penalty mathematical model of the mine car drive system in the deceleration stage in the current control cycle k are respectively established, including: an engine fuel consumption loss model, a power battery loss model, a reference angular velocity difference penalty model and a slip angular velocity difference penalty model, to comprehensively consider the drive torque and energy control, thereby reducing the maintenance cost; at the same time, since the present invention adopts steps (3-1) to (4-9), by performing SMA-PSO optimization on the optimization target mathematical model, the drive torque and power control of the mine car in each control cycle are both optimal values, which not only realizes efficient drive torque control, but also realizes corresponding power control, so that the driving performance of the mine car is optimal and the maintenance cost is lowest;

[0168] (3) Since the present invention establishes a mathematical model between the input power and angular velocity change of the braking resistor in step (2-2-1) and adds it to the mathematical model of the optimization target, accurate and efficient control of the braking resistor is achieved;

[0169] (4) Since the present invention adopts steps (2-1-2) and (2-2-2), a power balance equation constraint relationship is established and used as a part of the penalty function, so that the power consumption and input power of the mining car at any time are almost equal, reducing the fluctuation amplitude of the bus voltage. BRIEF DESCRIPTION OF THE DRAWINGS

[0170] Figure 1 It is a flow chart of the optimization control method of the mine car driving system based on SMA-PSO of the present invention;

[0171] Figure 2 is a detailed flow chart of step (3) in the method of the present invention;

[0172] Figure 3 It is a detailed flow chart of step (4) in the method of the present invention. DETAILED DESCRIPTION

[0173] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0174] like Figure 1 As shown, the present invention provides a mine car driving system optimization control method based on SMA-PSO, comprising the following steps:

[0175] (1) Obtain the angle of the accelerator pedal in the current control cycle k, the angle of the decelerator pedal in the current control cycle k, and the maximum angular acceleration of the left drive wheel and the right drive wheel (hereinafter referred to as the left drive wheel and the right drive wheel) of the driving system of the mine car, pre-process the angle of the accelerator pedal in the current control cycle k and the angle of the decelerator pedal in the current control cycle k, and obtain the reference angular velocity of the left drive wheel and the right drive wheel in the next control cycle k+1 according to the opening degree of the accelerator pedal after processing, the opening degree of the decelerator pedal after processing, the maximum angular acceleration of the left drive wheel and the right drive wheel of the driving system of the mine car, and the reference angular velocity of the left drive wheel and the right drive wheel in the current control cycle k.

[0176] This step includes the following sub-steps:

[0177] (1-1) Get the angle of the accelerator pedal in the current control cycle k and the angle of the deceleration pedal in the current control cycle k The angle of the accelerator pedal in the current control cycle k and the angle of the deceleration pedal in the current control cycle k are normalized to obtain the normalized opening of the accelerator pedal in the current control cycle k. and the normalized opening of the deceleration pedal in the current control cycle k

[0178] Specifically, the normalization of the angle of the accelerator pedal in the current control cycle k and the angle of the decelerator pedal in the current control cycle k is expressed by the following formula:

[0179]

[0180] in is the normalized opening of the accelerator pedal in the current control cycle k; is the angle of the accelerator pedal in the current control cycle k; θaccmax is the maximum angle of the accelerator pedal; θ accmin is the minimum angle of the accelerator pedal; is the normalized opening degree of the deceleration pedal in the current control cycle k; is the angle of the deceleration pedal in the current control cycle k; θ retmax is the maximum angle of the deceleration pedal; θ retmin is the minimum angle of the deceleration pedal; k is the current control cycle.

[0181] (1-2) Determine whether the normalized opening of the deceleration pedal obtained in step (1-1) is greater than 0. If so, proceed to step (1-4); otherwise, proceed to step (1-3);

[0182] (1-3) Determine whether the normalized opening of the accelerator pedal obtained in step (1-1) is greater than 0, if so, proceed to step (1-5), otherwise proceed to step (1-6);

[0183] (1-4) The value of the total pedal opening in the current control cycle k is set as the normalized opening of the deceleration pedal, and then proceed to step (1-7);

[0184] The specific steps of this step are:

[0185]

[0186] (1-5) The value of the total pedal opening in the current control cycle k is set as the normalized opening of the accelerator pedal, and then proceed to step (1-7);

[0187] The specific steps of this step are:

[0188]

[0189] (1-6) Set the value of the total pedal opening in the current control cycle k to 0, and then go to step (1-7);

[0190] The specific steps of this step are:

[0191]

[0192] in is the value of the total pedal opening in the current control cycle k, and its value range is [-1,1].

[0193] (1-7) Based on the value of the total pedal opening in the current control cycle k Maximum angular acceleration of the left and right drive wheels The reference angular velocity of the left and right driving wheels in the current control cycle k Get the reference angular velocity of the left and right drive wheels in the next control cycle k+1

[0194] Specifically, the reference angular velocity of the left drive wheel and the right drive wheel in the next control cycle k+1 is obtained by the following formula:

[0195]

[0196] The advantage of this step is that the two variables of the accelerator pedal and the decelerator pedal are mapped into one variable, which reduces the number of control quantities and facilitates the calculation of subsequent steps.

[0197] (2) According to the reference angular velocities of the left driving wheel and the right driving wheel in the next control cycle k+1 obtained in step (1), a comprehensive optimization target mathematical model of the mine car drive system in the acceleration phase and the deceleration phase in the current control cycle k is obtained.

[0198] This step includes the following sub-steps:

[0199] (2-1) Determine whether the value of the total pedal opening in the current control cycle k is less than or equal to 0. If so, proceed to step (2-2). Otherwise, obtain the comprehensive optimization target mathematical model of the mine car drive system in the acceleration stage in the current control cycle k according to the reference angular velocity of the left drive wheel and the right drive wheel obtained in step (1-7) in the next control cycle k+1. Then the process ends.

[0200] This step specifically includes the following sub-steps:

[0201] (2-1-1) Obtain the operating parameters of the mine car drive system in the acceleration stage, and calculate the penalty value and loss value corresponding to the difference between the angular velocity of the left drive wheel and the right drive wheel in the next control cycle k+1 and the reference angular velocity according to the operating parameters and the reference angular velocity of the left drive wheel and the right drive wheel in the next control cycle k+1 obtained in step (1-7), and obtain the preliminary optimization objective function F of the mine car drive system in the acceleration stage according to the obtained penalty value and loss value. 0acc .

[0202] The operating parameters of the mine car drive system in the acceleration stage include: the rotational inertia J of the left and right drive wheels of the mine car drive system, the control cycle duration t of the mine car drive system, the angular velocity of the left and right drive wheels in the current control cycle k and The driving torque of the left and right driving wheels in the current control cycle k and The load torque of the left and right driving wheels in the current control cycle k and The angular velocity update values ​​of the left and right drive wheels in the next control cycle k+1 and Reference angular velocity difference penalty coefficient C1, slip angular velocity difference penalty coefficient C2, fuel price C3 of the engine used in the mine car drive system (referred to as the engine), speed n corresponding to the minimum fuel consumption of the engine min , the charge and discharge loss coefficient C4 of the power battery (referred to as power battery) in the mine car drive system, the speed n of the engine in the current control cycle k k , and the charge and discharge power of the power battery in the current control cycle k

[0203] The specific step (2-1-1) is as follows: first, according to the rotational inertia J of the left driving wheel and the right driving wheel, the control cycle duration t of the mine car drive system, and the angular velocity of the left driving wheel and the right driving wheel in the current control cycle k, and The driving torque of the left and right driving wheels in the current control cycle k and The load torque of the left and right driving wheels in the current control cycle k and Get the updated angular velocity values ​​of the left and right driving wheels of the mine car drive system in the acceleration phase in the next control cycle k+1 respectively and

[0204]

[0205] Then, according to the reference angular velocity difference penalty coefficient C1, the angular velocity of the left driving wheel and the right driving wheel in the next control cycle k+1 and And the reference angular velocity of the left driving wheel and the right driving wheel obtained in step (1-7) in the next control cycle k+1 Get the penalty value corresponding to the difference between the angular velocity of the left drive wheel and the right drive wheel in the next control cycle k+1 and the reference angular velocity

[0206]

[0207] The value range of C1 is [0,1], and the preferred value is 0.9.

[0208] Then, according to the angular velocity of the left driving wheel and the right driving wheel in the next control cycle k+1 and Get the penalty value corresponding to the difference in angular velocity between the left drive wheel and the right drive wheel in the next control cycle k+1

[0209]

[0210] The value range of C2 is [0,1], and the preferred value is 0.85.

[0211] Then, according to the fuel price of the engine, the fuel consumption of the engine in the current control cycle k The engine speed n in the current control cycle k k 、Minimum fuel consumption of the engine G emin And the engine speed n corresponding to the lowest fuel consumption of the engine min Get the loss value of the engine in the current control cycle k

[0212]

[0213] where a ei (i=1, 2, 3) is the fitting coefficient of engine fuel consumption and engine speed, which is obtained by inputting the speed and fuel consumption data of the engine in the mine car drive system into MATLAB software and performing data fitting.

[0214] Then, according to the power battery charge and discharge loss coefficient C4 and the charge and discharge power of the power battery in the current control cycle k Get the loss value of the power battery in the current control cycle k

[0215]

[0216] The value range of C4 is [0,1], and the preferred value is 0.6. When The value of represents the power battery discharge power; When The value represents the charging power of the power battery.

[0217] Finally, the reference angular velocity difference penalty value obtained above is The penalty value corresponding to the difference in slip angular velocity Engine loss value Power battery loss value The sum is calculated to obtain the preliminary optimization objective function of the mine car drive system in the acceleration phase in the current control cycle k.

[0218] The advantage of this step is that the reference angular velocity difference penalty model, the slip angular velocity difference penalty model, the engine fuel consumption model and the power battery loss model are taken as preliminary optimization targets at the same time and comprehensively considered, that is, the influence of the slip rate of the left driving wheel and the right driving wheel of the mine car drive system on the optimization target and the influence of the loss of the generator on the optimization target are considered.

[0219] (2-1-2) According to the preliminary optimization objective function of the mine car drive system in the acceleration stage in the current control cycle k obtained in step (2-1-1), the inequality constraint relationship and equality constraint relationship of the controlled quantity in the preliminary optimization objective function are obtained.

[0220] The specific step (2-1-2) is as follows: first, according to the upper and lower boundary values ​​of the controlled quantity in the preliminary optimization objective function of the mine car drive system in the current control cycle k in the acceleration stage (the upper and lower boundary values ​​include: the maximum power consumption P of the left drive wheel and the right drive wheel motmax , Maximum engine speed n max , the maximum excitation current I of the generator of the mine car drive system max 、The maximum output power of the generator P Gmax , and the maximum charge and discharge power P of the power battery Bmax ) Determine the inequality constraint relationship of the controlled quantity in the preliminary optimization objective function;

[0221] The inequality constraint relationship of the controlled quantity in the preliminary optimization objective function specifically includes the following inequality constraints:

[0222] A. Inequality constraints on the power consumption of the left and right drive wheels in the current control cycle k:

[0223]

[0224] in and are the power consumption of the left and right driving wheels respectively; P motmax is the maximum power consumption of the left and right drive wheels,

[0225] B. Inequality constraint relationship of the engine speed in the current control cycle k:

[0226] 0≤n k ≤n max

[0227] C. The inequality constraint relationship between the excitation current of the generator in the current control cycle k and the output power of the generator in the current control cycle k:

[0228] 0≤I k ≤I max

[0229]

[0230] Among them I k is the excitation current of the generator in the current control cycle k, is the output power of the generator in the current control cycle k, I max and P Gmax is the maximum excitation current of the generator and the maximum output power of the generator.

[0231] D. The inequality constraint relationship of the charge and discharge power of the power battery in the current control cycle k:

[0232]

[0233] Where P Bmax It is the maximum charge and discharge power of the power battery.

[0234] Then, according to the intrinsic coupling relationship of the controlled quantity of the mine car drive system, the equation constraint relationship of the controlled quantity in the preliminary optimization objective function of the mine car drive system in the acceleration stage is obtained, which is expressed by the following formula:

[0235]

[0236] (2-1-3) The equality constraint relationship and inequality constraint relationship of the controlled quantity in the preliminary optimization objective function of the mine car drive system in the acceleration stage obtained in step (2-1-2) in the current control cycle k are respectively converted to obtain the penalty function corresponding to the equality constraint relationship The barrier function B corresponding to the inequality constraint x .

[0237] The specific step (2-1-3) is as follows: first, the corresponding penalty function is obtained according to the equality constraint relationship.

[0238]

[0239] Where C5 is the equality constraint penalty coefficient, which ranges from [0,1] and is preferably 0.4; a Gj (j=1,2) is the fitting value of generator output power and engine speed; a Gj (j=3, 4, 5) is the fitting value of the generator output power and the generator excitation current. The coefficient is obtained by inputting the engine speed, generator excitation current and generator output power data of the mine car drive system into MATLAB software for data fitting.

[0240] Then, the inequality constraint relationship is transformed into a logarithmic barrier to obtain the corresponding barrier function B x :

[0241] B x = -C x lg(x max ―x)

[0242] The value of x is n k ,I k ,as well as x max express n k ,I k ,as well as The maximum value in C x is the weight coefficient, and its value range is [0,1], C x The smaller it is, the closer the optimization result of x is to x. max ; and Preferably 0.3; Preferably 0.2; Preferably 0.1; Preferably 0.4;

[0243] Through the above formula, the speed obstacle function of the engine in the current control cycle k is obtained: The excitation current barrier function of the generator in the current control cycle k is The output power barrier function of the generator in the current control cycle k The power consumption barrier function of the left driving wheel in the current control cycle k The power consumption barrier function of the right driving wheel in the current control cycle k

[0244] Then, the inequality constraint relationship is transformed into a quadratic logarithmic barrier to obtain the corresponding barrier function

[0245]

[0246] The value of y is C y is the weight coefficient, the range is [0,1], and 0.1 is preferred. y The smaller it is, the closer the optimization result of y is to y max . The preferred value is 0.2. Through the above formula, the charging and discharging power barrier function of the power battery in the current control cycle k is obtained:

[0247] (2-1-4) Sum the penalty function and barrier function obtained in step (2-1-3) and the preliminary optimization objective function of the mine car drive system in the acceleration phase in the current control cycle k obtained in step (2-1-1) to obtain a comprehensive optimization objective mathematical model of the mine car drive system in the acceleration phase in the current control cycle k

[0248] Specifically, the comprehensive optimization objective mathematical model of the mine car drive system in the acceleration phase in the current control cycle k is It is expressed by the following formula:

[0249]

[0250] Where x is the controlled variable,

[0251] The advantage of this step is that a loss mathematical model, a penalty mathematical model and a barrier function mathematical model of the system put into operation in the current control cycle k of the mine car drive system in the acceleration stage are established to obtain a comprehensive optimization target mathematical model of the mine car drive system in the acceleration stage in the current control cycle k. The coupling between each controlled variable is comprehensively considered, and the constraints of the controlled quantity are converted into corresponding barrier functions, so that the boundaries of the controlled quantity are automatically avoided in the process of optimizing the control target.

[0252] (2-2) Obtain the comprehensive optimization target mathematical model of the mine car drive system in the deceleration stage in the current control cycle k according to the reference angular velocities of the left drive wheel and the right drive wheel in the next control cycle k+1 obtained in step (1-7) Then the process ends.

[0253] This step specifically includes the following sub-steps:

[0254] (2-2-1) Obtain the operating parameters of the mine car drive system in the deceleration stage, and calculate the penalty value and loss value corresponding to the difference between the angular velocity of the left drive wheel and the right drive wheel in the next control cycle k+1 and the reference angular velocity according to the operating parameters and the reference angular velocity of the left drive wheel and the right drive wheel in the next control cycle k+1 obtained in step (1-7), and obtain the preliminary optimization objective function F of the mine car drive system in the deceleration stage according to the obtained penalty value and loss value. 0ret .

[0255] The operating parameters of the mine car drive system in the deceleration stage include: the rotational inertia J of the left and right drive wheels of the mine car drive system, the control cycle duration t of the mine car drive system, the input power of the brake resistor in the current control cycle k The angular velocity of the left and right driving wheels in the current control cycle k and The driving torque of the left and right driving wheels in the current control cycle k and The load torque of the left and right driving wheels in the current control cycle k and The angular velocity update values ​​of the left and right drive wheels in the next control cycle k+1 and Reference angular velocity difference penalty coefficient C1, slip angular velocity difference penalty coefficient C2, fuel price C3 of the engine used in the mine car drive system (referred to as the engine), speed n corresponding to the minimum fuel consumption of the engine min , the charge and discharge loss coefficient C4 of the power battery (referred to as power battery) in the mine car drive system, the speed n of the engine in the current control cycle k k , and the charge and discharge power of the power battery in the current control cycle k

[0256] The specific step (2-2-1) is as follows: first, according to the rotational inertia J of the left driving wheel and the right driving wheel, the control cycle duration t of the mine car drive system, and the load torque of the left driving wheel and the right driving wheel in the current control cycle k, and The angular velocity of the left and right driving wheels in the current control cycle k and The driving torque of the left and right driving wheels in the current control cycle k and The input power of the left brake resistor and the right brake resistor in the current control cycle k and Get the updated angular velocity values ​​of the left and right driving wheels of the mine car drive system in the deceleration phase in the next control cycle k+1 respectively and

[0257]

[0258] Then, the penalty value of the reference angular velocity difference in the current control cycle k is obtained according to the formula in step (2-1-1): Penalty value of slip angular velocity difference in the current control cycle k The loss value of the engine in the current control cycle k And the loss value of the power battery in the current control cycle k

[0259] Then, the reference angular velocity difference obtained in the above step is calculated as the penalty value of the current control cycle k. Penalty value of slip angular velocity difference in the current control cycle k The loss value of the engine in the current control cycle k And the loss value of the power battery in the current control cycle k Sum to obtain the preliminary optimization objective function of the mine car drive system in the deceleration stage in the current control cycle k

[0260] (2-2-2) According to the preliminary optimization objective function of the mine car drive system in the deceleration stage in the current control cycle k obtained in step (2-2-1), the obstacle function corresponding to the inequality constraint relationship of the controlled quantity in the preliminary optimization objective function and the penalty function corresponding to the equality constraint relationship are obtained.

[0261] The specific step (2-2-2) is as follows: first, according to the upper and lower boundary values ​​of the controlled quantity in the preliminary optimization objective function of the mine car drive system in the deceleration stage in the current control cycle k (the upper and lower boundary values ​​include: the maximum power consumption P of the left drive wheel and the right drive wheel motmax , the maximum excitation current of the generator I max 、The maximum output power of the generator P Gmax , the maximum recovery power P of the kinetic energy recovery system regmax 、Maximum charge and discharge power of power battery P Bmax , Maximum input power P of the brake resistor Rmax ) Determine the inequality constraint relationship of the controlled quantity in the preliminary optimization objective function:

[0262] The inequality constraint relationship of the controlled quantity in the preliminary optimization objective function specifically includes the following inequality constraints:

[0263] A. Inequality constraints on the kinetic energy recovery power of the left and right driving wheels in the current control cycle k:

[0264]

[0265] in and is the kinetic energy recovery power of the left driving wheel and the right driving wheel in the current control cycle k;

[0266] P regmax It is the maximum kinetic energy recovery power of the left drive wheel and the right drive wheel. τ is the electromechanical conversion efficiency of the left drive wheel and the right drive wheel.

[0267] B. Inequality constraint relationship of the engine speed in the current control cycle k:

[0268] 0≤n k ≤n max

[0269] C. The inequality constraint relationship between the excitation current of the generator in the current control cycle k and the output power of the generator in the current control cycle k:

[0270] 0≤I k ≤I max

[0271]

[0272] D. The inequality constraint relationship of the charge and discharge power of the power battery in the current control cycle k:

[0273]

[0274] E. The input power inequality constraint of the left brake resistor and the right brake resistor in the current control cycle k is expressed by the following formula:

[0275]

[0276] in and is the input power of the left brake resistor and the right brake resistor in the current control cycle k; P Rmax It is the maximum input power of the braking resistor.

[0277] Then, according to the intrinsic coupling relationship of the controlled quantity of the mine car drive system, the equation constraint relationship of the controlled quantity in the preliminary optimization objective function of the mine car drive system in the deceleration stage in the current control cycle k is obtained:

[0278]

[0279] (2-2-3) Transform the inequality constraint relationship and equality constraint relationship of the controlled quantity in the preliminary optimization objective function of the mine car drive system in the deceleration stage obtained in step (2-2-2) in the current control cycle k to obtain the penalty function corresponding to the equality constraint relationship The barrier function B corresponding to the inequality constraint relationship x .

[0280] This step (2-2-3) is specifically as follows: first, according to the above equality constraint relationship, the penalty function corresponding to the equality constraint relationship can be obtained:

[0281]

[0282] Then, the inequality constraint relationship is transformed into a logarithmic barrier to obtain the corresponding barrier function B z :

[0283] B z = -C z lg(zmax ―z)

[0284] Where z is n k ,I k , as well as C z is the weight coefficient, which ranges from [0,1], C x The smaller it is, the closer the optimization result of x is to x. max . and Preferably 0.3; and Preferably it is 0.6.

[0285] Then, in the same way as in step (2-1-3), the inequality constraint relationship is transformed into a quadratic logarithmic barrier to obtain the corresponding barrier function

[0286]

[0287] (2-2-4) Sum the penalty function and barrier function obtained in step (2-2-3) and the preliminary optimization objective function of the mine car drive system in the deceleration stage in the current control cycle k obtained in step (2-2-1) to obtain a comprehensive optimization objective mathematical model of the mine car drive system in the deceleration stage in the current control cycle k

[0288] Specifically, the comprehensive optimization objective mathematical model of the mine car drive system in the deceleration stage in the current control cycle k is It is expressed by the following formula:

[0289]

[0290]

[0291] Where x is the controlled variable,

[0292] The advantage of this step is that the loss mathematical model, penalty mathematical model and obstacle function mathematical model of the mine car drive system in the deceleration stage in the current control cycle k are established to obtain the comprehensive optimization target mathematical model of the mine car drive system in the deceleration stage in the current control cycle k. A mathematical model of brake resistance and angular velocity was established to make brake control more precise.

[0293] (3) The comprehensive optimization target mathematical model of the mine car drive system in the acceleration stage and the deceleration stage obtained in step (2) is solved by the Slime Mould Algorithm (SMA) algorithm in the current control cycle k to obtain the optimal solution.

[0294] like Figure 2 As shown, this step includes the following sub-steps:

[0295] (3-1) Initialize the parameters of the SMA algorithm.

[0296] Specifically, this step includes: initializing the number of slime molds Pop = 20; initializing the random search probability z ra =0.2; initialize the maximum number of iterations C max =50; Initialize the lower boundary of the search space Bd low =0; Initialize the upper boundary of the search space where e i is the unit vector corresponding to each component in the search space; initialize the value of the controlled variable X = [T L0 T R0 n0P B0 P RR0 P RL0 ], where T L0 and T R0 are the initial values ​​of the left drive wheel torque and the right drive wheel torque respectively; n0 is the initial value of the engine speed; I0 is the initial value of the excitation current; P B0 is the initial value of the power battery output power; P RR0 The initial value of the power input to the right brake resistor; P RL0 is the initial value of the left brake resistor input power, and initializes the number of SMA iterations k SMA is 1; randomly generate the dth slime mold at the kth SMA The position in the search space at the iteration As the slime mold position corresponding to the slime mold, d∈[1,Pop]; generate a random number for the randomly generated d-th slime mold, the range of the random number is [0,1], as the random weight change rate r of the slime mold d .

[0297] (3-2) Determine the number of SMA iterations k in the parameters of the SMA algorithm initialized in step (3-1) SMA Is it greater than the maximum number of iterations C? max , if yes, go to step (3-9), otherwise go to step (3-3);

[0298] (3-3) According to the parameters of the SMA algorithm initialized in step (3-1) and the comprehensive optimization target mathematical model of the mine car drive system in the acceleration stage and the deceleration stage in the current control cycle k obtained in step (2), the kth position of each slime mold in the SMA algorithm is obtained. SMA The food concentration value at the iteration

[0299] Specifically, this step is to calculate the food concentration value by judging whether the mine car drive system is in the deceleration stage. If yes, then:

[0300]

[0301] If it is in the acceleration phase:

[0302]

[0303] in is the d-th slime mold at the kth SMA The food concentration during the second iteration is; is the d-th slime mold at the kth SMA The position in the search space at the iteration, d∈[1,Pop].

[0304] (3-4) For all slime molds obtained in step (3-3), SMA The food concentration value at the iteration Sort by the kth SMA The highest food concentration value at the iteration and its corresponding slime mold position (i.e. determine the maximum food concentration value After that, the corresponding slime mold can be determined, and then the corresponding slime mold), as well as the minimum food concentration value

[0305] (3-5) For the dth slime mold in the SMA algorithm, the random change rate r of the weight of the slime mold obtained in step (3-1) is d , and the highest food concentration value and the lowest food concentration value obtained in step (3-4), to obtain the value of the slime mold at the kth SMA The slime mold weight at iteration

[0306] Specifically, the new slime mold weight is expressed by

[0307]

[0308] in For all slime molds in the kth SMA The highest food concentration value at the iteration; For all slime molds in the kth SMA The lowest food concentration value at the iteration; is the d-th slime mold at the kth SMA The food concentration value at the iteration.

[0309] (3-6) For the dth slime mold in the SMA algorithm, the highest food concentration value obtained in step (3-4) is used to obtain the kth slime mold. SMA The position update value

[0310] Specifically, the slime mold SMA The position update value It is expressed by the following formula:

[0311]

[0312] in The slime mold is SMA The position update value of this time is the position update value.

[0313] (3-7) Randomly select two slime molds α and β from all slime molds in the SMA algorithm and obtain their corresponding positions as X α and X β For the dth slime mold in the SMA algorithm, the random weight change rate r of the slime mold obtained in step (3-1) is d , the upper boundary of the search space Bd up , the lower boundary of the search space Bd low , random search probability z ra , the slime mold position corresponding to the highest food concentration value obtained in step (3-4) The weight of slime mold obtained in step (3-5) The location update value obtained in step (3-6) Get the kth SMA +1 Slime Mold Position at Iteration

[0314] Specifically, this step uses the following formula to obtain the kth SMA +1 Slime Mold Position at Iteration

[0315]

[0316] where z ra is the random search probability, its value is 0.2; v g is the information interaction weight, which is a random number and its range is Obtained by a random number generator; v sReserve the weight for itself, which is a random number in the range Obtained from a random number generator.

[0317] (3-8) Set k SMA =k SMA +1, and return to step (3-2).

[0318] (3-9) According to step (3-3), all slime molds obtained are k SMA The food concentration values ​​at the iteration are sorted from high to low, and the first Food concentration value and its corresponding slime mold position as the best possible solution.

[0319] (4) Based on the possible optimal solution obtained in step (3), the improved PSO algorithm is used to obtain the final optimal solution, and the solution is used as the output signal of the drive system to control the mine car drive system.

[0320] like Figure 3 As shown, this step includes the following sub-steps:

[0321] (4-1) Initialize the parameters of the particle swarm algorithm according to the possible optimal solution obtained in step (3-9) to obtain initialized parameters.

[0322] Specifically, this step includes: initializing the number of particle swarms M PSO =Pop; Initialize the lower boundary of each particle's velocity where e u is the unit vector corresponding to each component of the velocity of each particle; the upper boundary of the velocity of each particle is initialized as Initialize the lower boundary x of each particle's position low =0; Initialize the upper boundary of each particle's position Initialize the maximum number of iterations S max is 20; obtained in step (3-9) Each slime mold position in the particles, and initialize the speed of each particle to 0, and initialize the number of PSO iterations k PSO =1.

[0323] (4-2) Determine the number of PSO iterations k PSO Is it greater than the maximum number of iterations S? max If yes, go to step (4-9), otherwise go to step (4-3).

[0324] (4-3) According to the food concentration value of the dth slime mold in the SMA algorithm obtained in step (3-3), the kth particle in the PSO algorithm is obtained. PSO The fitness at the iteration

[0325] Specifically, if the mine car drive system is in the deceleration stage, there are:

[0326]

[0327] If the minecart drive system is in the acceleration phase, then:

[0328]

[0329] where p∈[1,N PSO ].

[0330] (4-4) According to the PSO algorithm obtained in step (4-3), each particle in the kth PSO The fitness at the iteration Get the optimal fitness of the entire particle swarm and its corresponding position

[0331] This step is specifically as follows: for all particles obtained in step (4-3), PSO As for the fitness at the iteration, the position of the particle corresponding to the lowest fitness is taken as the position corresponding to the optimal fitness of the entire particle swarm. And take the minimum fitness as the optimal fitness L of the entire particle swarm b .

[0332] (4-5) Determine the pth particle in the PSO algorithm at the kth PSO Is the fitness during iteration less than the kth PSO -1 The fitness at the time of iteration, if yes, then the kth PSO The iterated position is set to the pth particle at the kth PSO The position corresponding to the iterated individual optimal value Then go to step (4-6); otherwise, PSO The position of the p-th particle at the k-th iteration is set PSO The position corresponding to the iterated individual optimal value Then proceed to step (4-6).

[0333] The kth order of each particle is obtained by the above method. PSO The position corresponding to the individual optimal value at the iteration

[0334] (4-6) The fitness of each particle obtained according to step (4-3) And the optimal fitness of the entire particle swarm obtained in step (4-4) Get the pth particle at the kth PSO Compression factor during iteration The pth particle is in the kth PSO Individual learning factor during iteration and the pth particle is at the kth PSO Group learning factor during iteration

[0335] Specifically, this step first obtains the pth particle at the kth PSO Individual learning factor during iteration and the pth particle is at the kth PSO Group learning factor during iteration

[0336]

[0337] Then, the p-th particle in the k-th particle is calculated by the following formula PSO Compression factor during iteration

[0338]

[0339] in is the pth particle at the kth PSO The auxiliary factor during iteration is equal to:

[0340]

[0341] (4-7) According to the calculation in (4-6), the pth particle is in the kth PSO Individual learning factor during iteration The pth particle is in the kth PSO Group learning factor during iteration The pth particle is in the kth PSO Compression factor during iteration Get the pth particle at the kth PSO +1 position and velocity at iteration

[0342] The specific steps of this step are as follows: first, calculate the p-th particle at the k-th PSO +1 Iteration position and the pth particle is at the kth PSO +1 Iteration speed It is expressed by the following formula:

[0343]

[0344] Where r1 and r2 are random numbers, both in the range of [0,1], obtained by the random number generator;

[0345]

[0346] in is the pth particle at the kth PSO +1 Updated speed at iteration; is the pth particle at the kth PSO The speed of iteration; is the pth particle at the kth PSO The position when iterating.

[0347] Then, calculate the p-th particle at the k-th PSO +1 position at iteration

[0348]

[0349] (4-8) Set k PSO =k PSO +1, and return to step (4-2);

[0350] (4-9) The position corresponding to the optimal fitness of the entire particle swarm is obtained As the final optimal solution, it is used as the output signal of the drive system to control each subsystem of the mine car drive system.

[0351] The advantage of steps (4-1) to (4-9) is that the initial position of the particle swarm is set near the optimal solution and suboptimal solution of the previous step, and the number of particles at different positions decreases as the food concentration value decreases, which improves the particle swarm's ability to deeply develop the optimal solution of the previous step, while also developing it near the suboptimal solution, thus avoiding the particle swarm algorithm from converging to a local optimal solution; at the same time, by introducing a compression factor, the convergence speed of the PSO algorithm is improved, and the ability of the PSO algorithm to solve problems is accelerated.

[0352] The advantage of steps (3-1) to (4-9) is that they utilize the strong global development capability of SMA and the fast convergence speed of PSO after the compression factor is introduced, so that the optimization process reduces the probability of falling into a local optimal solution and improves the accuracy of the optimization results.

[0353] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A mine car driving system optimization control method based on SMA-PSO, characterized in that: The following steps are involved: (1) Obtain the angle of the accelerator pedal in the current control cycle k, the angle of the decelerator pedal in the current control cycle k, and the maximum angular acceleration of the left drive wheel and the right drive wheel of the mine car's drive system, pre-process the angle of the accelerator pedal in the current control cycle k and the angle of the decelerator pedal in the current control cycle k, and obtain the reference angular velocity of the left drive wheel and the right drive wheel in the next control cycle k+1 according to the opening degree of the accelerator pedal after processing, the opening degree of the decelerator pedal after processing, the maximum angular acceleration of the left drive wheel and the right drive wheel of the mine car's drive system, and the reference angular velocity of the left drive wheel and the right drive wheel in the current control cycle k. (2) According to the reference angular velocities of the left driving wheel and the right driving wheel in the next control cycle k+1 obtained in step (1), a comprehensive optimization target mathematical model of the mine car drive system in the acceleration phase and the deceleration phase in the current control cycle k is obtained. (3) The comprehensive optimization target mathematical model of the mine car drive system in the acceleration stage and the deceleration stage obtained in step (2) is solved by the slime mold algorithm SMA to obtain the optimal solution. (4) Based on the possible optimal solution obtained in step (3), the improved PSO algorithm is used to obtain the final optimal solution, and the solution is used as the output signal of the drive system to control the mine car drive system.

2. The SMA-PSO-based optimization control method for a mine car driving system according to claim 1 is characterized in that: Step (1) includes the following sub-steps: (1-1) Get the angle of the accelerator pedal in the current control cycle k and the deceleration pedal angle in the current control cycle k The angle of the accelerator pedal in the current control cycle k and the angle of the deceleration pedal in the current control cycle k are normalized to obtain the normalized opening of the accelerator pedal in the current control cycle k. and the normalized opening of the deceleration pedal in the current control cycle k in is the normalized opening of the accelerator pedal in the current control cycle k; is the angle of the accelerator pedal in the current control cycle k; θ accmax is the maximum angle of the accelerator pedal; θ accmin is the minimum angle of the accelerator pedal; is the normalized opening degree of the deceleration pedal in the current control cycle k; is the angle of the deceleration pedal in the current control cycle k; θ retmax is the maximum angle of the deceleration pedal; θ retmin is the minimum angle of the deceleration pedal; k is the current control cycle. (1-2) Determine whether the normalized opening of the deceleration pedal obtained in step (1-1) is greater than 0. If so, proceed to step (1-4); otherwise, proceed to step (1-3); (1-3) Determine whether the normalized opening of the accelerator pedal obtained in step (1-1) is greater than 0, if so, proceed to step (1-5), otherwise proceed to step (1-6); (1-4) The total pedal opening value in the current control cycle k Set to the normalized opening of the deceleration pedal Then go to step (1-7); (1-5) The total pedal opening value in the current control cycle k Set to the normalized opening of the accelerator pedal Then go to step (1-7); (1-6) The total pedal opening value in the current control cycle k Set to 0, then go to step (1-7); (1-7) Based on the value of the total pedal opening in the current control cycle k Maximum angular acceleration of the left and right drive wheels The reference angular velocity of the left and right driving wheels in the current control cycle k Get the reference angular velocity of the left and right drive wheels in the next control cycle k+1 。 3. According to the SMA-PSO-based optimization control method for a mine car driving system according to claim 1 or 2, step (2) comprises the following sub-steps: (2-1) Determine whether the value of the total pedal opening in the current control cycle k is less than or equal to 0. If so, proceed to step (2-2). Otherwise, obtain the comprehensive optimization target mathematical model of the mine car drive system in the acceleration stage in the current control cycle k according to the reference angular velocity of the left drive wheel and the right drive wheel obtained in step (1-7) in the next control cycle k+1. Then the process ends. (2-2) Obtain the comprehensive optimization target mathematical model of the mine car drive system in the deceleration stage in the current control cycle k according to the reference angular velocities of the left drive wheel and the right drive wheel in the next control cycle k+1 obtained in step (1-7) Then the process ends.

4. According to the SMA-PSO-based optimization control method for a mine car drive system according to any one of claims 1 to 3, step (2-1) comprises the following sub-steps: (2-1-1) Obtain the operating parameters of the mine car drive system in the acceleration stage, and calculate the penalty value and loss value corresponding to the difference between the angular velocity of the left drive wheel and the right drive wheel in the next control cycle k+1 and the reference angular velocity according to the operating parameters and the reference angular velocity of the left drive wheel and the right drive wheel in the next control cycle k+1 obtained in step (1-7), and obtain the preliminary optimization objective function F of the mine car drive system in the acceleration stage according to the obtained penalty value and loss value. 0acc ; in The operating parameters of the mine car drive system in the acceleration stage include: the rotational inertia J of the left and right drive wheels of the mine car drive system, the control cycle duration t of the mine car drive system, the angular velocity of the left and right drive wheels in the current control cycle k and The driving torque of the left and right driving wheels in the current control cycle k and The load torque of the left and right driving wheels in the current control cycle k and The angular velocity update values ​​of the left and right drive wheels in the next control cycle k+1 and Reference angular velocity difference penalty coefficient C1, slip angular velocity difference penalty coefficient C2, fuel price C3 of the engine used in the mine car drive system, and speed n corresponding to the minimum fuel consumption of the engine min , the charge and discharge loss coefficient C4 of the power battery in the mine car drive system, the speed n of the engine in the current control cycle k k , and the charge and discharge power of the power battery in the current control cycle k (2-1-2) According to the preliminary optimization objective function of the mine car drive system in the acceleration stage in the current control cycle k obtained in step (2-1-1), the inequality constraint relationship and equality constraint relationship of the controlled quantity in the preliminary optimization objective function are obtained. (2-1-3) The equality constraint relationship and inequality constraint relationship of the controlled quantity in the preliminary optimization objective function of the mine car drive system in the acceleration stage obtained in step (2-1-2) in the current control cycle k are respectively converted to obtain the penalty function corresponding to the equality constraint relationship The barrier function B corresponding to the inequality constraint x . (2-1-4) Sum the penalty function and barrier function obtained in step (2-1-3) and the preliminary optimization objective function of the mine car drive system in the acceleration phase in the current control cycle k obtained in step (2-1-1) to obtain a comprehensive optimization objective mathematical model of the mine car drive system in the acceleration phase in the current control cycle k 5. The SMA-PSO-based optimization control method for a mine car driving system according to claim 4 is characterized in that: Step (2-1-1) is as follows: first, according to the moment of inertia J of the left driving wheel and the right driving wheel, the control cycle duration t of the mine car drive system, and the angular velocity of the left driving wheel and the right driving wheel in the current control cycle k, and The driving torque of the left and right driving wheels in the current control cycle k and The load torque of the left and right driving wheels in the current control cycle k and Get the updated angular velocity values ​​of the left and right driving wheels of the mine car drive system in the acceleration phase in the next control cycle k+1 respectively and Then, according to the reference angular velocity difference penalty coefficient C1, the angular velocity of the left driving wheel and the right driving wheel in the next control cycle k+1 and And the reference angular velocity of the left driving wheel and the right driving wheel obtained in step (1-7) in the next control cycle k+1 Get the penalty value corresponding to the difference between the angular velocity of the left drive wheel and the right drive wheel in the next control cycle k+1 and the reference angular velocity The value range of C1 is [0,1], and the preferred value is 0.

9. Then, according to the angular velocity of the left driving wheel and the right driving wheel in the next control cycle k+1 and Get the penalty value corresponding to the difference in angular velocity between the left drive wheel and the right drive wheel in the next control cycle k+1 The value range of C2 is [0,1], and the preferred value is 0.

85. Then, according to the fuel price of the engine, the fuel consumption of the engine in the current control cycle k The engine speed n in the current control cycle k k 、Minimum fuel consumption of the engine G emin And the engine speed n corresponding to the lowest fuel consumption of the engine min Get the loss value of the engine in the current control cycle k where a ei (i=1, 2, 3) is the fitting coefficient of engine fuel consumption and engine speed, which is obtained by inputting the speed and fuel consumption data of the engine in the mine car drive system into MATLAB software and performing data fitting. Then, according to the power battery charge and discharge loss coefficient C4 and the charge and discharge power of the power battery in the current control cycle k Get the loss value of the power battery in the current control cycle k The value range of C4 is [0,1], and the preferred value is 0.

6. When The value of represents the power battery discharge power; When The value represents the charging power of the power battery. Finally, the reference angular velocity difference penalty value obtained above is The penalty value corresponding to the difference in slip angular velocity Engine loss value Power battery loss value The sum is calculated to obtain the preliminary optimization objective function of the mine car drive system in the acceleration phase in the current control cycle k. Step (2-1-2) is specifically as follows: first, according to the upper and lower boundary values ​​of the controlled quantity in the preliminary optimization objective function of the mine car drive system in the acceleration stage, the inequality constraint relationship of the controlled quantity in the preliminary optimization objective function of the current control cycle k is determined, and the upper and lower boundary values ​​include: the maximum power consumption P of the left driving wheel and the right driving wheel motmax , Maximum engine speed n max , the maximum excitation current I of the generator of the mine car drive system max , the maximum output power P of the generator Gmax , and the maximum charge and discharge power P of the power battery Bmax ; The inequality constraint relationship of the controlled quantity in the preliminary optimization objective function specifically includes the following inequality constraints: A. Inequality constraints on the power consumption of the left and right drive wheels in the current control cycle k: in and are the power consumption of the left and right driving wheels respectively; P motmax is the maximum power consumption of the left and right drive wheels, B. Inequality constraint relationship of the engine speed in the current control cycle k: 0≤n k ≤n max C. The inequality constraint relationship between the excitation current of the generator in the current control cycle k and the output power of the generator in the current control cycle k: 0≤I k ≤I max Among them I k is the excitation current of the generator in the current control cycle k, is the output power of the generator in the current control cycle k, I max and P Gmax is the maximum excitation current of the generator and the maximum output power of the generator. D. The inequality constraint relationship of the charge and discharge power of the power battery in the current control cycle k: Where P Bmax It is the maximum charge and discharge power of the power battery. Then, according to the intrinsic coupling relationship of the controlled quantity of the mine car drive system, the equation constraint relationship of the controlled quantity in the preliminary optimization objective function of the mine car drive system in the acceleration stage is obtained, which is expressed by the following formula: 。 Step (2-1-3) is as follows: first, the corresponding penalty function is obtained according to the equality constraint relationship. Where C5 is the equality constraint penalty coefficient, which ranges from [0,1] and is preferably 0.4; a Gj (j=1,2) is the fitting value of generator output power and engine speed; a Gj (j=3, 4, 5) is the fitting value of the generator output power and the generator excitation current. The coefficient is obtained by inputting the engine speed, generator excitation current and generator output power data of the mine car drive system into the MATLAB software for data fitting. Then, the inequality constraint relationship is transformed into a logarithmic barrier to obtain the corresponding barrier function B x : B x =―C x lg(x max -X) The value of x is n k ,I k ,as well as x max express n k ,I k ,as well as The maximum value in C x is the weight coefficient, and its value range is [0,1], C x The smaller it is, the closer the optimization result of x is to x. max ; and Preferably 0.3; Preferably 0.2; Preferably 0.1; Preferably 0.4; Then, the inequality constraint relationship is transformed into a quadratic logarithmic barrier to obtain the corresponding barrier function The value of y is C y is the weight coefficient, the range is [0,1], and 0.1 is preferred. y The smaller it is, the closer the optimization result of y is to y max . Preferably it is 0.

2. The comprehensive optimization target mathematical model of the mine car drive system in the acceleration phase in the current control cycle k in step (2-1-4) It is expressed by the following formula: Where x is the controlled variable, 6. According to the SMA-PSO-based optimization control method for a mine car driving system according to claim 5, step (2-2) specifically comprises the following sub-steps: (2-2-1) Obtain the operating parameters of the mine car drive system in the deceleration stage, and calculate the penalty value and loss value corresponding to the difference between the angular velocity of the left drive wheel and the right drive wheel in the next control cycle k+1 and the reference angular velocity according to the operating parameters and the reference angular velocity of the left drive wheel and the right drive wheel in the next control cycle k+1 obtained in step (1-7), and obtain the preliminary optimization objective function F of the mine car drive system in the deceleration stage according to the obtained penalty value and loss value. 0ret , where the operating parameters of the mine car drive system in the deceleration stage include: The moment of inertia J of the left and right drive wheels of the mine car drive system, the control cycle duration t of the mine car drive system, and the input power of the brake resistor in the current control cycle k The angular velocity of the left and right driving wheels in the current control cycle k and The driving torque of the left and right driving wheels in the current control cycle k and The load torque of the left and right driving wheels in the current control cycle k and The angular velocity update values ​​of the left and right drive wheels in the next control cycle k+1 and Reference angular velocity difference penalty coefficient C1, slip angular velocity difference penalty coefficient C2, fuel price C3 of the engine used in the mine car drive system, and speed n corresponding to the minimum fuel consumption of the engine min , the charge and discharge loss coefficient C4 of the power battery in the mine car drive system, the speed n of the engine in the current control cycle k k , and the charge and discharge power of the power battery in the current control cycle k (2-2-2) According to the preliminary optimization objective function of the mine car drive system in the deceleration stage in the current control cycle k obtained in step (2-2-1), the obstacle function corresponding to the inequality constraint relationship of the controlled quantity in the preliminary optimization objective function and the penalty function corresponding to the equality constraint relationship are obtained. (2-2-3) Transform the inequality constraint relationship and equality constraint relationship of the controlled quantity in the preliminary optimization objective function of the mine car drive system in the deceleration stage obtained in step (2-2-2) in the current control cycle k to obtain the penalty function corresponding to the equality constraint relationship The barrier function B corresponding to the inequality constraint relationship x . (2-2-4) Sum the penalty function and barrier function obtained in step (2-2-3) and the preliminary optimization objective function of the mine car drive system in the deceleration stage in the current control cycle k obtained in step (2-2-1) to obtain a comprehensive optimization objective mathematical model of the mine car drive system in the deceleration stage in the current control cycle k 7. The SMA-PSO-based optimization control method for a mine car driving system according to claim 6 is characterized in that: The specific step (2-2-1) is as follows: first, according to the rotational inertia J of the left driving wheel and the right driving wheel, the control cycle duration t of the mine car drive system, and the load torque of the left driving wheel and the right driving wheel in the current control cycle k, and The angular velocity of the left and right driving wheels in the current control cycle k and The driving torque of the left and right driving wheels in the current control cycle k and The input power of the left brake resistor and the right brake resistor in the current control cycle k and Get the updated angular velocity values ​​of the left and right driving wheels of the mine car drive system in the deceleration phase in the next control cycle k+1 respectively and Then, the penalty value of the reference angular velocity difference in the current control cycle k is obtained according to the formula in step (2-1-1): Penalty value of slip angular velocity difference in the current control cycle k The loss value of the engine in the current control cycle k And the loss value of the power battery in the current control cycle k Then, the reference angular velocity difference obtained in the above step is calculated as the penalty value of the current control cycle k. Penalty value of slip angular velocity difference in the current control cycle k The loss value of the engine in the current control cycle k And the loss value of the power battery in the current control cycle k Sum to obtain the preliminary optimization objective function of the mine car drive system in the deceleration stage in the current control cycle k Step (2-2-2) is specifically as follows: first, according to the upper and lower boundary values ​​of the controlled quantity in the preliminary optimization objective function of the mine car drive system in the deceleration stage in the current control cycle k, the inequality constraint relationship of the controlled quantity in the preliminary optimization objective function is determined, and the upper and lower boundary values ​​include: the maximum power consumption P of the left driving wheel and the right driving wheel motmax , the maximum excitation current of the generator I max , the maximum output power P of the generator Gmax , the maximum recovery power P of the kinetic energy recovery system regmax 、Maximum charge and discharge power of power battery P Bmax , Maximum input power P of the brake resistor Rmax : The inequality constraint relationship of the controlled quantity in the preliminary optimization objective function specifically includes the following inequality constraints: A. Inequality constraints on the kinetic energy recovery power of the left and right driving wheels in the current control cycle k: in and is the kinetic energy recovery power of the left driving wheel and the right driving wheel in the current control cycle k; P regmax It is the maximum kinetic energy recovery power of the left drive wheel and the right drive wheel. τ is the electromechanical conversion efficiency of the left drive wheel and the right drive wheel. B. Inequality constraint relationship of the engine speed in the current control cycle k: 0≤n k ≤n max C. The inequality constraint relationship between the excitation current of the generator in the current control cycle k and the output power of the generator in the current control cycle k: 0≤I k ≤I max D. The inequality constraint relationship of the charge and discharge power of the power battery in the current control cycle k: E. The input power inequality constraint of the left brake resistor and the right brake resistor in the current control cycle k is expressed by the following formula: in and is the input power of the left brake resistor and the right brake resistor in the current control cycle k; P Rmax It is the maximum input power of the braking resistor. Then, according to the intrinsic coupling relationship of the controlled quantity of the mine car drive system, the equation constraint relationship of the controlled quantity in the preliminary optimization objective function of the mine car drive system in the deceleration stage in the current control cycle k is obtained: ； Step (2-2-3) is specifically as follows: first, according to the above equality constraint relationship, the penalty function corresponding to the equality constraint relationship can be obtained: Then, the inequality constraint relationship is transformed into a logarithmic barrier to obtain the corresponding barrier function B z : B z =―C z lg(z max ―z) Where z is n k ,I k , as well as C z is the weight coefficient, which ranges from [0,1], C x The smaller it is, the closer the optimization result of x is to x. max . and Preferably 0.3; and Preferably it is 0.

6. Then, in the same way as in step (2-1-3), the inequality constraint relationship is transformed into a quadratic logarithmic barrier to obtain the corresponding barrier function ； The comprehensive optimization target mathematical model of the mine car drive system in the deceleration stage in step (2-2-4) in the current control cycle k It is expressed by the following formula: Where x is the controlled variable, 8. The SMA-PSO-based optimization control method for a mine car driving system according to claim 7, characterized in that: Step (3) includes the following sub-steps: (3-1) Initialize the parameters of the SMA algorithm, including: initializing the number of slime molds Pop = 20; initializing the random search probability z ra =0.2; initialize the maximum number of iterations C max =50; Initialize the lower boundary of the search space Bd low =0; Initialize the upper boundary of the search space where e l is the unit vector corresponding to each component in the search space; initialize the value of the controlled variable X = [T L0 T R0 n0 I0 P B0 P RR0 P RL0 ], where T L0 and T R0 are the initial values ​​of the left drive wheel torque and the right drive wheel torque respectively; n0 is the initial value of the engine speed; I0 is the initial value of the excitation current; P B0 is the initial value of the power battery output power; P RR0 The initial value of the power input to the right brake resistor; P RL0 is the initial value of the left brake resistor input power, and initializes the number of SMA iterations k SMA is 1; randomly generate the dth slime mold at the kth SMA The position in the search space at the iteration As the slime mold position corresponding to the slime mold, d∈[1,Pop]; generate a random number for the randomly generated d-th slime mold, the range of the random number is [0,1], as the random weight change rate r of the slime mold d . (3-2) Determine the number of SMA iterations k in the parameters of the SMA algorithm initialized in step (3-1) SMA Is it greater than the maximum number of iterations C? max , if yes, go to step (3-9), otherwise go to step (3-3); (3-3) According to the parameters of the SMA algorithm initialized in step (3-1) and the comprehensive optimization target mathematical model of the mine car drive system in the acceleration stage and the deceleration stage in the current control cycle k obtained in step (2), the kth position of each slime mold in the SMA algorithm is obtained. SMA The food concentration value at the iteration If the mine car drive system is in the deceleration stage, then: If it is in the acceleration phase: in is the d-th slime mold at the kth SMA The food concentration during the second iteration is; is the d-th slime mold at the kth SMA The position in the search space at the iteration, d∈[1,Pop]. (3-4) For all slime molds obtained in step (3-3), SMA The food concentration value at the iteration Sort by the kth SMA The highest food concentration value at the iteration and its corresponding slime mold position and the minimum food concentration (3-5) For the dth slime mold in the SMA algorithm, the random change rate r of the weight of the slime mold obtained in step (3-1) is d , and the highest food concentration value and the lowest food concentration value obtained in step (3-4), to obtain the value of the slime mold at the kth SMA The slime mold weight at iteration in For all slime molds in the kth SMA The highest food concentration value at the iteration; For all slime molds in the kth SMA The lowest food concentration value at iteration . (3-6) For the dth slime mold in the SMA algorithm, the highest food concentration value obtained in step (3-4) is used to obtain the kth slime mold. SMA The position update value in The slime mold is SMA The position update value of this time is the position update value. (3-7) Randomly select two slime molds α and β from all slime molds in the SMA algorithm and obtain their corresponding positions as X α and X β For the dth slime mold in the SMA algorithm, the random weight change rate r of the slime mold obtained in step (3-1) is d , the upper boundary of the search space Bd up , the lower boundary of the search space Bd low , random search probability z ra , the slime mold position corresponding to the highest food concentration value obtained in step (3-4) The weight of slime mold obtained in step (3-5) The location update value obtained in step (3-6) Get the kth SMA +1 Slime Mold Position at Iteration where z ra is the random search probability, its value is 0.2; v g is the information interaction weight, which is a random number and its range is Obtained by a random number generator; v s Reserve the weight for itself, which is a random number in the range Obtained from a random number generator. (3-8) Set k SMA =k SMA +1, and return to step (3-2). (3-9) According to step (3-3), all slime molds obtained are k SMA The food concentration values ​​at the iteration are sorted from high to low, and the first Food concentration value and its corresponding slime mold position as the best possible solution.

9. The SMA-PSO-based optimization control method for a mine car driving system according to claim 8, characterized in that: Step (4) includes the following sub-steps: (4-1) Initializing the parameters of the particle swarm algorithm according to the possible optimal solution obtained in step (3-9) to obtain the initialized parameters, including: initializing the number of particle swarms N PSO =Pop; Initialize the lower boundary of each particle's velocity where e u is the unit vector corresponding to each component of the velocity of each particle; the upper boundary of the velocity of each particle is initialized as Initialize the lower bound x of each particle's position low =0; Initialize the upper boundary of each particle's position Initialize the maximum number of iterations S max is 20; obtained in step (3-9) Each slime mold position in the particles, and initialize the speed of each particle to 0, and initialize the number of PSO iterations k PSO =1. (4-2) Determine the number of PSO iterations k PSO Is it greater than the maximum number of iterations S? max If yes, go to step (4-9), otherwise go to step (4-3). (4-3) According to the food concentration value of the dth slime mold in the SMA algorithm obtained in step (3-3), the kth particle in the PSO algorithm is obtained. PSO The fitness at the iteration If the mine car drive system is in the deceleration stage, then: If the minecart drive system is in the acceleration phase, then: where p∈[1,k PSO ]. (4-4) According to the PSO algorithm obtained in step (4-3), each particle in the kth PSO The fitness at the iteration Get the optimal fitness of the entire particle swarm and its corresponding position For all particles obtained in step (4-3), PSO As for the fitness at the iteration, the position of the particle corresponding to the lowest fitness is taken as the position corresponding to the optimal fitness of the entire particle swarm. And take the minimum fitness as the optimal fitness L of the entire particle swarm b . (4-5) Determine the pth particle in the kth particle of the PSO algorithm PSO Is the fitness during iteration less than the kth PSO -1 The fitness at the iteration, if yes, then the kth PSO The iterated position is set to the pth particle at the kth PSO The position corresponding to the iterated individual optimal value Then go to step (4-6); otherwise, PSO The position of the p-th particle at the k-th iteration is set PSO The position corresponding to the iterated individual optimal value Then proceed to step (4-6). (4-6) The fitness of each particle obtained according to step (4-3) And the optimal fitness of the entire particle swarm obtained in step (4-4) Get the pth particle at the kth PSO Compression factor during iteration The pth particle is in the kth PSO Individual learning factor during iteration and the pth particle is at the kth PSO Group learning factor during iteration (4-7) According to the calculation in (4-6), the pth particle is in the kth PSO Individual learning factor during iteration The pth particle is in the kth PSO Group learning factor during iteration The pth particle is in the kth PSO Compression factor during iteration Get the pth particle at the kth PSO +1 position and velocity at iteration (4-8) Set k PSO =k PSO +1, and return to step (4-2); (4-9) The position corresponding to the optimal fitness of the entire particle swarm is obtained As the final optimal solution, it is used as the output signal of the drive system to control each subsystem of the mine car drive system.

10. The SMA-PSO-based optimization control method for a mine car driving system according to claim 9, characterized in that: Step (4-6) is as follows: first, obtain the pth particle at the kth PSO Individual learning factor during iteration and the pth particle is at the kth PSO Group learning factor during iteration Then, the p-th particle in the k-th particle is calculated by the following formula PSO Compression factor during iteration in is the pth particle at the kth PSO The auxiliary factor during iteration is equal to: ； Step (4-7) is as follows: first, calculate the p-th particle at the k-th PSO +1 Iteration position and the pth particle is at the kth PSO +1 Iteration speed It is expressed by the following formula: Where r1 and r2 are random numbers, both in the range of [0,1], obtained by the random number generator; in is the pth particle at the kth PSO +1 Updated speed at iteration; is the pth particle at the kth PSO The speed of iteration; is the pth particle at the kth PSO The position when iterating. Then, calculate the p-th particle at the k-th PSO +1 position at iteration 。

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