An Optimization Control Method for Mine Car Drive System Based on SMA-PSO

Abstract

The present invention discloses an optimization control method for a mine car drive system based on SMA-PSO, including: obtaining the angle of the accelerator pedal in the current control period k, the angle of the deceleration pedal in the current control period k, and the maximum angular accelerations of the left and right drive wheels of the drive system of the mine car, preprocessing 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, and obtaining the reference angular velocities of the left and right drive wheels in the next control period k+1, obtaining the comprehensive optimization target mathematical model of the mine car drive system in the current control period k in the acceleration stage and the deceleration stage, and solving the comprehensive optimization target mathematical model of the mine car drive system in the current control period k in the acceleration stage and the deceleration stage through the slime mold algorithm SMA. The present invention can solve the technical problems of discontinuous and large fluctuations of the controlled quantity and inaccurate control effect in the existing discrete logic threshold control strategy.

Description

Technical Field

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

[0002] The main task of the drive system of a mine car is to control the drive torque of the inverter at the current moment of the mine car by the opening degree of the acceleration and deceleration pedals collected. At the same time, it is also necessary to control the generator to ensure that the generator can provide stable and reliable energy supply for the inverter. The drive system of the mine car mainly controls the drive torque and energy of the mine car according to the acceleration and deceleration pedals. However, how to ensure stable operation of the drive system while minimizing the maintenance cost is an important issue.

[0003] The existing drive system control strategies mainly include discrete logic threshold control strategies, control strategies with vehicle slip ratio as the control target, control strategies combining regenerative braking and mechanical braking, and energy distribution strategies with the lowest 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 where the current operating state of the vehicle is located; the control strategy with vehicle slip ratio as the control target sets the control target of controlling the slip ratios of the left and right drive wheels near the optimal slip ratio to control the drive torques of the left and right drive wheels; the control strategy combining regenerative braking and mechanical braking distributes the braking torque to the regenerative braking system and the mechanical braking system according to the optimal ratio to achieve vehicle deceleration; the energy distribution strategy with the lowest energy consumption controls the optimal output powers of the generator and the power battery by considering engine fuel consumption and power battery loss.

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

[0005] (1) Since the discrete logic threshold control strategy controls each controlled quantity separately and does not consider the coupling between the controlled quantities, and the control strategies between different thresholds are not continuous, the fluctuation amplitude of the controlled quantity is large.

[0006] (2) The control strategy with vehicle slip ratio as the control target only considers drive torque control and does not consider the energy distribution of the vehicle, resulting in a high maintenance cost of the energy supply system.

[0007] (3) Since the mine car uses a combination of braking resistors and braking feedback for braking, and the control strategy combining braking feedback and mechanical braking does not target the control of braking resistors, 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 and does not consider the change in energy consumption caused by drive torque control, resulting in a large fluctuation range of the DC bus voltage. Summary of the Invention

[0009] In view of the above deficiencies or improvement requirements of the prior art, the present invention provides an optimized control method for a mine car drive system based on SMA-PSO. Its purpose is to solve the technical problems of the existing discrete logic threshold control strategy. Since each controlled quantity is controlled 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 of the controlled quantities and inaccurate control effects. And the existing control strategy with the vehicle slip ratio as the control target only considers drive torque control without considering the energy distribution of the vehicle, resulting in high maintenance costs 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 resistors 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 and does not consider the change in energy consumption caused by drive torque control, resulting in a large fluctuation range of the DC bus voltage.

[0010] To achieve the above object, according to one aspect of the present invention, an optimized control method for a mine car drive system based on SMA-PSO is provided, including 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 accelerations of the left and right drive wheels of the mine car drive system. Preprocess 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 velocities of the left and right drive wheels in the next control cycle k + 1 according to the processed opening of the accelerator pedal, the processed opening of the decelerator pedal, the maximum angular accelerations of the left and right drive wheels of the mine car drive system, and the reference angular velocities of the left and right drive wheels in the current control cycle k.

[0012] (2) Obtain the comprehensive optimization target mathematical model of the mine car drive system in the current control cycle k in the acceleration stage and the deceleration stage according to the reference angular velocities of the left and right drive wheels in the next control cycle k + 1 obtained in step (1).

[0013] (3) Solve the comprehensive optimization objective mathematical model of the mine car drive system in the acceleration stage and the deceleration stage obtained in step (2) in the current control cycle k through the slime mold algorithm SMA to obtain the optimal solution.

[0014] (4) According to the optimal solution obtained in step (3), use the improved PSO algorithm to obtain the final optimal solution, and use it as the output signal of the drive system to control the mine car drive system.

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

[0016] (1-1) Obtain 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 Normalize 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 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] where 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 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) Judge whether the normalized opening of the deceleration pedal obtained in step (1-1) is greater than 0. If it is, enter step (1-4); otherwise, enter step (1-3);

[0020] (1-3) Judge whether the normalized opening of the accelerator pedal obtained in step (1-1) is greater than 0. If it is, enter step (1-5); otherwise, enter step (1-6);

[0021] (1-4) Set the value of the total pedal opening in the current control cycle k to the normalized opening of the deceleration pedal Then proceed to step (1-7);

[0022] (1-5) Set the value of the total pedal opening in the current control cycle k as the opening of the accelerator pedal after normalization Then proceed to step (1-7);

[0023] (1-6) Set the value of the total pedal opening in the current control cycle k to 0, and then proceed to step (1-7);

[0024] (1-7) Based on the value of the total pedal opening obtained in the current control cycle k the maximum angular accelerations of the left and right drive wheels the reference angular velocities of the left and right drive wheels in the current control cycle k Obtain the reference angular velocities of the left and right drive wheels in the next control cycle k+1

[0025]

[0026] Preferably, step (2) includes the following sub-steps:

[0027] (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 objective mathematical model of the mine car drive system in the acceleration stage in the current control cycle k according to the reference angular velocities of the left and right drive wheels obtained in step (1-7) Then the process ends.

[0028] (2-2) Obtain the comprehensive optimization objective 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 and right drive wheels obtained in step (1-7) Then the process ends.

[0029] Preferably, step (2-1) includes the following sub-steps:

[0030] (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 velocities of the left and right drive wheels and the reference angular velocities in the next control cycle k+1 according to the operating parameters and the reference angular velocities of the left and right drive wheels obtained in step (1-7). Then, 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 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, the angular velocities of the left and right drive wheels in the current control cycle k and the driving torques of the left and right drive wheels in the current control cycle k and the load torques of the left and right drive wheels in the current control cycle k and the updated values of the angular velocities of the left and right drive wheels in the next control cycle k + 1 and the reference angular velocity difference penalty coefficient C1, the slip angular velocity difference penalty coefficient C2, the oil price C3 of the fuel used by the engine of the mine car drive system, the rotational speed n corresponding to the minimum fuel consumption of the engine min , the charge-discharge loss coefficient C4 of the power battery in the mine car drive system, the rotational speed n of the engine in the current control cycle k k , and the charge-discharge power of the power battery in the current control cycle k

[0031] (2-1-2) Obtain the inequality constraint relationship and the equality constraint relationship of the controlled variables in the preliminary optimization objective function according to the preliminary optimization objective function of the mine car drive system in the acceleration stage obtained in step (2-1-1).

[0032] (2-1-3) Convert the equality constraint relationship and the inequality constraint relationship of the controlled variables in the preliminary optimization objective function of the mine car drive system in the acceleration stage obtained in step (2-1-2) respectively, so as to obtain the penalty function corresponding to the equality constraint relationship and the barrier function B x .

[0033] (2-1-4) Sum up the penalty function and the barrier function obtained in step (2-1-3) and the preliminary optimization objective function of the mine car drive system in the acceleration stage obtained in step (2-1-1), so as to obtain the comprehensive optimization objective mathematical model of the mine car drive system in the acceleration stage in the current control cycle k

[0034] Preferably, step (2-1-1) is specifically as follows. First, according to the moment of inertia J of the left and right drive wheels, the control cycle duration t of the mine car drive system, the angular velocities of the left and right drive wheels in the current control cycle k and the driving torques of the left and right drive wheels in the current control cycle k and The load torques of the left drive wheel and the right drive wheel in the current control period k and respectively obtain the updated angular velocity values of the left drive wheel and the right drive wheel of the mine car drive system in the acceleration stage in the next control period k + 1 and

[0035]

[0036] Subsequently, according to the reference angular velocity difference penalty coefficient C1, the angular velocities of the left drive wheel and the right drive wheel in the next control period k + 1 and and the reference angular velocities of the left drive wheel and the right drive wheel in the next control period k + 1 obtained in steps (1 - 7) obtain the penalty value corresponding to the difference between the angular velocities and the reference angular velocities of the left drive wheel and the right drive wheel in the next control period k + 1

[0037]

[0038] where the value range of C1 is [0, 1], preferably 0.9

[0039] Subsequently, according to the angular velocities of the left drive wheel and the right drive wheel in the next control period k + 1 and obtain the penalty value corresponding to the difference between the angular velocities of the left drive wheel and the right drive wheel in the next control period k + 1

[0040]

[0041] where the value range of C2 is [0, 1], preferably 0.85

[0042] Subsequently, according to the oil price of the fuel used by the engine, the fuel consumption of the engine in the current control period k the rotational speed n of the engine in the current control period k k , the minimum fuel consumption G of the engine emin and the engine rotational speed n corresponding to the minimum fuel consumption of the engine min obtain the loss value of the engine in the current control period k

[0043]

[0044] where a ei(i = 1, 2, 3) is the fitting coefficient of the engine fuel consumption and the engine speed, and this coefficient is obtained by inputting the speed and fuel consumption data in the engine of the mine truck drive system into the MATLAB software for data fitting.

[0045] Subsequently, according to the charge and discharge loss coefficient C4 of the power battery and the charge and discharge power of the power battery in the current control cycle k Obtain the loss value of the power battery in the current control cycle k

[0046]

[0047] Among them, the value range of C4 is [0, 1], and preferably 0.6. When It indicates that the power battery is in the discharge state. At this time The value represents the discharge power of the power battery; When it indicates that the power battery is in the charging state. At this time The value represents the charging power of the power battery.

[0048] Finally, sum up the reference angular velocity difference penalty value The penalty value corresponding to the difference in the slip angular velocity The engine loss value The power battery loss value To obtain the preliminary optimization objective function of the mine truck drive system in the acceleration stage in the current control cycle k

[0049] Step (2-1-2) is specifically as follows. First, determine the inequality constraint relationship of the controlled quantity in the preliminary optimization objective function according to the upper and lower boundary values of the controlled quantity in the preliminary optimization objective function of the mine truck drive system in the acceleration stage in the current control cycle k. The upper and lower boundary values include: the maximum consumption power P motmax Of the left and right drive wheels, the maximum engine speed n max The maximum excitation current I of the generator of the mine truck 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 ;

[0050] The inequality constraint relationship of the controlled quantity in the preliminary optimization objective function specifically includes the following inequality constraints:

[0051] A. The inequality constraint relationship of the consumption power of the left and right drive wheels in the current control cycle k:

[0052]

[0053] Among them and are the power consumptions of the left drive wheel and the right drive wheel respectively; P motmax is the maximum power consumption of the left drive wheel and the right drive wheel,

[0054] B. Inequality constraint relationship of the engine speed in the current control cycle k:

[0055] 0 ≤ n k ≤ n max

[0056] C. Inequality constraint relationship of 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:

[0057] 0 ≤ I k ≤ I max

[0058]

[0059] where 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 are the maximum excitation current of the generator and the maximum output power of the generator.

[0060] D. Inequality constraint relationship of the charge and discharge power of the power battery in the current control cycle k:

[0061]

[0062] where P Bmax is the maximum charge and discharge power of the power battery.

[0063] Subsequently, according to the inherent coupling relationship of the controlled variables of the mine car drive system, the equality constraint relationship of the controlled variables in the preliminary optimization objective function of the mine car drive system in the acceleration stage in the current control cycle k is obtained, which is expressed by the following formula:

[0064]

[0065] Step (2-1-3) is specifically as follows. First, the corresponding penalty function is obtained according to the equality constraint relationship

[0066]

[0067] where C5 is the equality constraint penalty coefficient, and its range is [0, 1], preferably 0.4; a Gj (j = 1, 2) is the fitting value of the generator output power and the engine speed; aGj (j = 3, 4, 5) is the fitted value of the generator output power and the generator excitation current. This coefficient is obtained by inputting the rotational speed of the engine of the mine car drive system, the excitation current of the generator, and the generator output power data into MATLAB software for data fitting.

[0068] Then, perform a logarithmic barrier transformation on the inequality constraint relationship to obtain the corresponding barrier function B x :

[0069] B x = -C x lg(x max - x)

[0070] where the value of x is n k 、I k 、and x max represents n k 、I k 、and the maximum value among them, C x is the weight coefficient, and its value range is [0, 1]. The smaller C x , the closer the optimization result of x is to x max ; and are preferably 0.3; is preferably 0.2; is preferably 0.1; is preferably 0.4;

[0071] Subsequently, perform a quadratic logarithmic barrier transformation on the inequality constraint relationship to obtain the corresponding barrier function

[0072]

[0073] where the value of y is C y is the weight coefficient, with a range of [0, 1], preferably 0.1. The smaller C y , the closer the optimization result of y is to y max . is preferably 0.2.

[0074] The comprehensive optimization objective mathematical model of the mine car drive system in the acceleration stage in step (2 - 1 - 4) at the current control cycle k is expressed by the following formula:

[0075]

[0076]

[0077] where x is the controlled variable, and are the input powers of the left braking resistor and the right braking resistor in the current control period k.

[0078] Preferably, step (2-2) specifically includes the following sub-steps:

[0079] (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 velocities of the left drive wheel and the right drive wheel in the next control period k+1 based on the operating parameters and the reference angular velocities of the left drive wheel and the right drive wheel obtained in step (1-7). Then, 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 moments of inertia J of the left drive wheel and the right drive wheel of the mine car drive system, the control period duration t of the mine car drive system, the input power of the braking resistor in the current control period k the angular velocities of the left drive wheel and the right drive wheel in the current control period k and the driving torques of the left drive wheel and the right drive wheel in the current control period k and the load torques of the left drive wheel and the right drive wheel in the current control period k and the angular velocity update values of the left drive wheel and the right drive wheel in the next control period k+1 and the reference angular velocity difference penalty coefficient C1, the slip angular velocity difference penalty coefficient C2, the oil price C3 of the fuel used by the engine of the mine car drive system, the 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 period k k , and the charge and discharge power of the power battery in the current control period k

[0080] (2-2-2) Obtain the barrier 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 according to the preliminary optimization objective function of the mine car drive system in the deceleration stage obtained in step (2-2-1).

[0081] (2-2-3) Transform the inequality constraint relationship and equality constraint relationship of the controlled variables 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 and the barrier function B corresponding to the inequality constraint relationship x .

[0082] (2-2-4) Sum up 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 obtained in step (2-2-1) in the current control cycle k to obtain the comprehensive optimization objective mathematical model of the mine car drive system in the deceleration stage in the current control cycle k

[0083] Preferably, this step (2-2-1) is specifically as follows. First, according to the moment of inertia J of the left and right drive wheels, the control cycle duration t of the mine car drive system, the load torques of the left and right drive wheels in the current control cycle k and the angular velocities of the left and right drive wheels in the current control cycle k and the drive torques of the left and right drive wheels in the current control cycle k and the input powers of the left and right braking resistors in the current control cycle k and respectively obtain the updated values of the angular velocities of the left and right drive wheels of the mine car drive system in the deceleration stage in the next control cycle k+1 and

[0084]

[0085] Subsequently, obtain the penalty value of the reference angular velocity difference in the current control cycle k according to the formula in step (2-1-1) the penalty value of the 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

[0086] Thereafter, the penalty value of the reference angular velocity difference obtained in the above steps in the current control cycle k the penalty value of the 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 up to obtain the preliminary optimization objective function of the mine car drive system in the deceleration stage in the current control period k

[0087] Step (2-2-2) specifically is as follows. First, determine the inequality constraint relationship of the controlled variables in the preliminary optimization objective function according to the upper and lower boundary values of the controlled variables in the preliminary optimization objective function of the mine car drive system in the deceleration stage in the current control period k. The upper and lower boundary values include: the maximum power consumption P of the left and right drive wheels motmax , the maximum excitation current I of the generator max , the maximum output power P of the generator Gmax , the maximum recovery power P of the kinetic energy recovery system regmax , the maximum charge and discharge power P of the power battery Bmax , the maximum input power P of the braking resistor Rmax :

[0088] The inequality constraint relationship of the controlled variables in the preliminary optimization objective function specifically includes the following inequality constraints:

[0089] A. Inequality constraint of the kinetic energy recovery power of the left and right drive wheels in the current control period k:

[0090]

[0091] Where and are the kinetic energy recovery powers of the left and right drive wheels in the current control period k; P regmax is the maximum kinetic energy recovery power of the left and right drive wheels. τ is the electromechanical conversion efficiency of the left and right drive wheels.

[0092] B. Inequality constraint relationship of the engine speed in the current control period k:

[0093] 0 ≤ n k ≤ n max

[0094] C. Inequality constraint relationship of the excitation current of the generator and the output power of the generator in the current control period k:

[0095] 0 ≤ I k ≤ I max

[0096]

[0097] D. Inequality constraint relationship of the charge and discharge power of the power battery in the current control period k:

[0098]

[0099] E. The input power inequality constraints of the left braking resistor and the right braking resistor in the current control period k are expressed by the following formula:

[0100]

[0101] where P Rmax is the maximum input power of the braking resistor.

[0102] Subsequently, according to the inherent coupling relationship of the controlled variables of the mine car drive system, the equality constraint relationship of the controlled variables in the preliminary optimization objective function of the mine car drive system in the deceleration stage in the current control period k is obtained:

[0103]

[0104] Specifically, in step (2-2-3), first, according to the above equality constraint relationship, the penalty function corresponding to the equality constraint relationship can be obtained

[0105]

[0106] Then, logarithmic barrier transformation is performed on the inequality constraint relationship to obtain the corresponding barrier function B z :

[0107] B z =-C z lg(z max -z)

[0108] where z takes values of n k , I k , and C z is the weight coefficient, and its range is [0, 1]. The smaller C x , the closer the optimization result of x is to x max . and are preferably 0.3; and are preferably 0.6.

[0109] Subsequently, in the same manner as in step (2-1-3), quadratic logarithmic barrier transformation is performed on the inequality constraint relationship to obtain the corresponding barrier function

[0110]

[0111] The comprehensive optimization objective mathematical model of the mine car drive system in the deceleration stage in the current control period k in step (2-2-4) is represented by the following formula:

[0112]

[0113]

[0114] where x is the controlled variable,

[0115] Preferably, step (3) includes the following sub-steps:

[0116] (3-1) Initialize the parameters of the SMA algorithm, including: initialize the number of slime molds Pop = 20; initialize the random search probability z ra = 0.2; initialize the maximum number of iterations C max = 50; initialize the lower boundary Bd of the search space 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 n0I0P 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 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 output power of the power battery; P RR0 is the initial value of the power input of the right braking resistor; P RL0 is the initial value of the power input of the left braking resistor, initialize the SMA iteration number k SMA as 1; randomly generate the position of the d-th slime mold in the search space at the k SMA -th iteration as the slime mold position corresponding to this slime mold, d ∈ [1, Pop]; generate a random number for the randomly generated d-th slime mold, and the range of the random number is [0, 1], as the random weight change rate r d .

[0117] (3-2) Judge whether the SMA iteration number k SMA in the parameters of the SMA algorithm after initialization in step (3-1) is greater than the maximum number of iterations C max , if so, go to step (3-9), otherwise go to step (3-3);

[0118] (3-3) According to the parameters of the SMA algorithm initialized in step (3-1) and the comprehensive optimization objective mathematical model of the mine car drive system in the acceleration stage and deceleration stage obtained in step (2) in the current control period k, obtain the food concentration value of each slime mold in the SMA algorithm at the k SMA th iteration Where if the mine car drive system is in the deceleration stage, there is:

[0119]

[0120] If it is in the acceleration stage, there is:

[0121]

[0122] Where is the food concentration of the d-th slime mold at the k SMA th iteration; is the position of the d-th slime mold in the search space at the k SMA th iteration, d ∈ [1, Pop].

[0123] (3-4) Sort the food concentration values of all slime molds obtained in step (3-3) at the k SMA th iteration to obtain the highest food concentration value at the k SMA th iteration and its corresponding slime mold position and the lowest food concentration value

[0124] (3-5) For the d-th slime mold in the SMA algorithm, according to the random change rate r of the weight of this slime mold obtained in step (3-1) d , and the highest food concentration value and the lowest food concentration value obtained in step (3-4), obtain the slime mold weight of this slime mold at the k SMA th iteration

[0125]

[0126] Where is the highest food concentration value of all slime molds at the k SMA th iteration; is the lowest food concentration value of all slime molds at the k SMA th iteration.

[0127] (3-6) For the d-th slime mold in the SMA algorithm, obtain the position update value of this slime mold at the k SMA th time according to the highest food concentration value obtained in step (3-4)

[0128]

[0129] wherein is the position update value of the slime mold at the k SMA -th time.

[0130] (3-7) Randomly select two slime molds α and β from all the slime molds of the SMA algorithm, and obtain their corresponding positions as X α and X β . For the d-th slime mold in the SMA algorithm, according to the random weight change rate r d of the slime mold obtained in step (3-1), the upper boundary Bd up of the search space, the lower boundary Bd low of the search space, the random search probability z ra , the slime mold position corresponding to the highest food concentration value obtained in step (3-4) the slime mold weight obtained in step (3-5) the position update value obtained in step (3-6) obtain the slime mold position at the (k SMA +1)-th iteration of this slime mold

[0131]

[0132] where z ra is the random search probability, and 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 is the self-preservation weight, which is a random number, and its range is obtained by a random number generator.

[0133] (3-8) Set k SMA = k SMA +1, and return to step (3-2).

[0134] (3-9) Sort the food concentration values of all the slime molds at the k SMA -th iteration obtained in step (3-3) in descending order, and take the first food concentration values and their corresponding slime mold positions as the optimal solution.

[0135] Preferably, step (4) includes the following sub-steps:

[0136] (4-1) Initialize the parameters of the particle swarm optimization algorithm according to the optimal solution obtained in step (3-9) to obtain the initialized parameters, including: initialize the number of particle swarms N PSO = Pop; initialize the lower boundary of the velocity of each particle where e u is the unit vector corresponding to each component of the velocity of each particle; initialize the upper boundary of the velocity of each particle as Initialize the lower boundary x of the position of each particle low = 0; initialize the upper boundary of the position of each particle Initialize the maximum number of iterations S max as 20; at each slime mold position among the slime mold positions obtained in step (3-9), generate particles, and initialize the velocity of each particle to 0, initialize the PSO iteration number k PSO = 1.

[0137] (4-2) Determine whether the PSO iteration number k PSO is greater than the maximum number of iterations S max , if so, go to step (4-9), otherwise go to step (4-3).

[0138] (4-3) Obtain the fitness of each particle in the PSO algorithm at the k PSO -th iteration according to the food concentration value of the d-th slime mold in the SMA algorithm obtained in step (3-3) where if the mine car drive system is in the deceleration stage, then there is:

[0139]

[0140] If the mine car drive system is in the acceleration stage, then there is:

[0141]

[0142] where p ∈ [1, N PSO .

[0143] (4-4) Obtain the optimal fitness of the entire particle swarm PSO and its corresponding position according to the fitness of each particle in the PSO algorithm at the k -th iteration obtained in step (4-3) where for the fitness of all particles obtained in step (4-3) at the k -th iteration, take the position where the particle with the lowest fitness is located as the position corresponding to the optimal fitness of the entire particle swarm PSO ​And use this lowest fitness as the optimal fitness L of the entire particle swarm b .

[0144] (4 - 5) Determine whether the fitness of the p-th particle in the PSO algorithm at the k PSO -th iteration is less than the fitness at the k PSO -1-th iteration. If so, set the position at the k PSO -th iteration as the position corresponding to the individual optimal value of the p-th particle at the k PSO -th iteration, and then proceed to step (4 - 6); otherwise, set the position at the k -1-th iteration as the position corresponding to the individual optimal value of the p-th particle at the k PSO -th iteration, and then proceed to step (4 - 6). PSO Iteration, and then proceed to step (4 - 6). Then proceed to step (4 - 6).

[0145] (4 - 6) According to the fitness of each particle obtained in step (4 - 3) and the optimal fitness of the entire particle swarm obtained in step (4 - 4) Obtain the compression factor of the p-th particle at the k PSO -th iteration The individual learning factor of the p-th particle at the k PSO -th iteration and the swarm learning factor of the p-th particle at the k PSO -th iteration

[0146] (4 - 7) According to the individual learning factor of the p-th particle at the k PSO -th iteration calculated in (4 - 6) The individual learning factor of the p-th particle at the k PSO -th iteration The swarm learning factor of the p-th particle at the k PSO -th iteration The compression factor of the p-th particle at the k PSO +1-th iteration to obtain the position and velocity of the p-th particle at the k

[0147] (4 - 8) Set k PSO = k PSO +1, and return to step (4 - 2);

[0148] (4 - 9) Take the position corresponding to the optimal fitness of the entire particle swarm obtained as the final optimal solution, and use it as the output signal of the drive system to control each subsystem of the mine car drive system.

[0149] Preferably, step (4 - 6) is specifically as follows: First, obtain the p-th particle at the kPSO Individual learning factor during iteration and the p-th particle at the k-th PSO Group learning factor during iteration

[0150]

[0151] Then, calculate the compression factor of the p-th particle at the k-th PSO iteration through the following formula

[0152]

[0153] where is the auxiliary factor of the p-th particle at the k-th PSO iteration and is equal to:

[0154]

[0155] Specifically, steps (4-7) are as follows. First, calculate the position of the p-th particle at the (k + 1)-th PSO iteration and the velocity of the p-th particle at the (k + 1)-th PSO iteration which are represented by the following formula:

[0156]

[0157] where r1 and r2 are random numbers, both in the range of [0, 1], obtained from a random number generator;

[0158]

[0159] where is the updated velocity of the p-th particle at the (k + 1)-th PSO iteration; is the velocity of the p-th particle at the k-th PSO iteration; is the position of the p-th particle at the k-th PSO iteration.

[0160] Subsequently, calculate the position of the p-th particle at the (k + 1)-th PSO iteration

[0161]

[0162] Generally speaking, compared with the prior art, the above technical solution conceived by the present invention can achieve the following beneficial effects:

[0163] (1) Since the present invention establishes an optimization objective mathematical model for the mine car drive system in steps (1) and (2), and directly converts the constraint relationship into a barrier function and adds it to the optimization objective mathematical model, when the mine car is controlled each time, the fluctuation amplitude of the controlled quantity is limited within the threshold, reducing the fluctuation amplitude of the controlled quantity;

[0164] (2) Since the present invention adopts steps (2-1) and (2-2) to establish a loss and penalty mathematical model of the mine car drive system in the acceleration stage and a loss and penalty mathematical model of the mine car drive system in the deceleration stage in the current control cycle k respectively, including: engine fuel consumption loss model, power battery loss model, reference angular velocity difference penalty model and slip angular velocity difference penalty model, to comprehensively consider drive torque and energy control, reducing the maintenance cost; at the same time, since the present invention adopts steps (3-1) to (4-9), through SMA-PSO optimization solution of the optimization objective mathematical model, the drive torque and power control of the mine car in each control cycle are both optimal values, not only realizing efficient drive torque control, but also realizing the corresponding power control, making the drive performance of the mine car optimal and the maintenance cost the lowest;

[0165] (3) Since the present invention establishes a mathematical model between the input power of the braking resistor and the angular velocity change in step (2-2-1) and adds it to the optimization objective mathematical model, realizing precise and efficient control of the braking resistor;

[0166] (4) Since the present invention adopts steps (2-1-2) and (2-2-2) to establish a power balance equation constraint relationship and takes it as part of the penalty function, the power consumption and input power of the mine car are almost equal at any time, reducing the fluctuation amplitude of the bus voltage. Description of the Drawings

[0167] Figure 1 is a flowchart of the optimization control method for the mine car drive system based on SMA-PSO of the present invention;

[0168] Figure 2 is a detailed flowchart of step (3) in the method of the present invention;

[0169] Figure 3 is a detailed flowchart of step (4) in the method of the present invention. Detailed Embodiments

[0170] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be 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 used 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.

[0171] As Figure 1 shown, the present invention provides an optimized control method for a mine car drive system based on SMA-PSO, including the following steps:

[0172] (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 accelerations of the left and right drive wheels of the mine car drive system (hereinafter referred to as the left and right drive wheels for short), preprocess 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 velocities of the left and right drive wheels in the next control cycle k + 1 according to the processed opening of the accelerator pedal, the processed opening of the decelerator pedal, the maximum angular accelerations of the left and right drive wheels of the mine car drive system, and the reference angular velocities of the left and right drive wheels in the current control cycle k.

[0173] This step includes the following sub-steps:

[0174] (1-1) Obtain 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 Perform normalization processing on 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 to obtain the normalized opening of the accelerator pedal in the current control cycle k and the normalized opening of the decelerator pedal in the current control cycle k

[0175] Specifically, the normalization processing 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 represented by the following formula:

[0176]

[0177] where 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 of the decelerator 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.

[0178] (1 - 2) Determine whether the normalized opening of the deceleration pedal obtained in step (1 - 1) is greater than 0. If so, go to step (1 - 4); otherwise, go to step (1 - 3);

[0179] (1 - 3) Determine whether the normalized opening of the acceleration pedal obtained in step (1 - 1) is greater than 0. If so, go to step (1 - 5); otherwise, go to step (1 - 6);

[0180] (1 - 4) Set the value of the total pedal opening in the current control cycle k to the normalized opening of the deceleration pedal, and then transfer to step (1 - 7);

[0181] Specifically, this step is as follows:

[0182]

[0183] (1 - 5) Set the value of the total pedal opening in the current control cycle k to the normalized opening of the acceleration pedal, and then transfer to step (1 - 7);

[0184] Specifically, this step is as follows:

[0185]

[0186] (1 - 6) Set the value of the total pedal opening in the current control cycle k to 0, and then transfer to step (1 - 7);

[0187] Specifically, this step is as follows:

[0188]

[0189] where is the value of the total pedal opening in the current control cycle k, and its value range is [-1, 1].

[0190] (1 - 7) According to the obtained value of the total pedal opening in the current control cycle k the maximum angular accelerations of the left and right drive wheels the reference angular velocities of the left and right drive wheels in the current control cycle k Obtain the reference angular velocities of the left and right drive wheels in the next control cycle k + 1

[0191] Specifically, the reference angular velocities of the left and right drive wheels in the next control cycle k + 1 are obtained through the following formula

[0192]

[0193] The advantage of this step is that the two variables of the accelerator pedal and the decelerator pedal are mapped into one variable, reducing the number of control variables and facilitating the calculation in the subsequent steps.

[0194] (2) Obtain the comprehensive optimization objective mathematical model of the mine car drive system in the current control period k in the acceleration stage and the deceleration stage according to the reference angular velocities of the left drive wheel and the right drive wheel in the next control period k + 1 obtained in step (1).

[0195] This step includes the following sub-steps:

[0196] (2-1) Judge whether the value of the total pedal opening in the current control period k is less than or equal to 0. If so, go to step (2-2); otherwise, obtain the comprehensive optimization objective mathematical model of the mine car drive system in the acceleration stage in the current control period k according to the reference angular velocities of the left drive wheel and the right drive wheel in the next control period k + 1 obtained in step (1-7). Then the process ends.

[0197] This step specifically includes the following sub-steps:

[0198] (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 velocities of the left drive wheel and the right drive wheel in the next control period k + 1 and the reference angular velocities according to the operating parameters and the reference angular velocities of the left drive wheel and the right drive wheel in the next control period k + 1 obtained in step (1-7). Then, 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 。

[0199] The operating parameters of the mine car drive system in the acceleration stage include: the moment of inertia J of the left drive wheel and the right drive wheel of the mine car drive system, the control period duration t of the mine car drive system, the angular velocities of the left drive wheel and the right drive wheel in the current control period k and the driving torques of the left drive wheel and the right drive wheel in the current control period k and the load torques of the left drive wheel and the right drive wheel in the current control period k and the angular velocity update values of the left drive wheel and the right drive wheel in the next control period k + 1 and Refer to the angular velocity difference penalty coefficient C1, the slip angular velocity difference penalty coefficient C2, the oil price C3 of the fuel used by the engine of the mine car drive system (abbreviated as the engine), and the rotational speed n corresponding to the minimum fuel consumption of the engine min 、the charge and discharge loss coefficient C4 of the power battery (abbreviated as the power battery) in the mine car drive system, the rotational 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

[0200] Specifically, in this step (2-1-1), first, according to the moment of inertia J of the left and right drive wheels, the duration t of the control cycle of the mine car drive system, the angular velocities and of the left and right drive wheels in the current control cycle k and the driving torques and of the left and right drive wheels in the current control cycle k and

[0201]

[0202] respectively obtain the updated values of the angular velocities of the left and right drive wheels of the mine car drive system in the next control cycle k+1 during the acceleration phase and and the reference angular velocities of the left and right drive wheels in the next control cycle k+1 obtained in step (1-7)

[0203]

[0204] where the value range of C1 is [0,1], preferably 0.9.

[0205] Subsequently, according to the angular velocities and of the left and right drive wheels in the next control cycle k+1

[0206]

[0207] Among them, the value range of C2 is [0, 1], preferably 0.85.

[0208] Subsequently, according to the oil price of the fuel used by the engine, the fuel consumption of the engine in the current control cycle k the rotational speed n of the engine in the current control cycle k k and the minimum fuel consumption G of the engine emin as well as the engine rotational speed n corresponding to the minimum fuel consumption of the engine min obtain the loss value of the engine in the current control cycle k

[0209]

[0210] where a ei (i = 1, 2, 3) is the fitting coefficient of the engine fuel consumption and the engine rotational speed, and this coefficient is obtained by inputting the rotational speed and fuel consumption data in the engine of the mine car drive system into MATLAB software for data fitting.

[0211] Subsequently, 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 obtain the loss value of the power battery in the current control cycle k

[0212]

[0213] Among them, the value range of C4 is [0, 1], preferably 0.6. When it indicates that the power battery is in the discharge state, and at this time the value represents the discharge power of the power battery; it indicates that the power battery is in the charging state, and at this time the value represents the charging power of the power battery.

[0214] Finally, sum up the reference angular velocity difference penalty value the penalty value corresponding to the difference in the slip angular velocity the engine loss value the power battery loss value to obtain the preliminary optimization objective function of the mine car drive system in the acceleration stage in the current control cycle k

[0215] 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 loss model, and the power battery loss model are simultaneously used as the preliminary optimization objectives for comprehensive consideration, that is, it considers the influence of the slip rates of the left and right drive wheels of the mine car drive system on the optimization objective and also considers the influence of the generator loss on the optimization objective.

[0216] (2-1-2) Obtain the inequality constraint relationship and equality constraint relationship of the controlled variables in the preliminary optimization objective function of the mine car drive system in the acceleration stage obtained in step (2-1-1) for the current control period k.

[0217] Specifically, in this step (2-1-2), first, according to the upper and lower boundary values of the controlled variables in the preliminary optimization objective function of the mine car drive system in the acceleration stage for the current control period k (the upper and lower boundary values include: the maximum power consumption P of the left and right drive wheels motmax , the 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 ), determine the inequality constraint relationship of the controlled variables in the preliminary optimization objective function;

[0218] The inequality constraint relationship of the controlled variables in the preliminary optimization objective function specifically includes the following inequality constraints:

[0219] A. The inequality constraint relationship of the power consumption of the left and right drive wheels in the current control period k:

[0220]

[0221] Where and are the power consumptions of the left and right drive wheels respectively; P motmax is the maximum power consumption of the left and right drive wheels,

[0222] B. The inequality constraint relationship of the engine speed in the current control period k:

[0223] 0 ≤ n k ≤ n max

[0224] C. The inequality constraint relationship of the excitation current of the generator and the output power of the generator in the current control period k:

[0225] 0 ≤ I k ≤ I max

[0226]

[0227] Where I k is the excitation current of the generator in the current control period k, is the output power of the generator in the current control period k, Imax and P Gmax are the maximum excitation current of the generator and the maximum output power of the generator respectively.

[0228] D. The charge and discharge power inequality constraint relationship of the power battery in the current control period k:

[0229]

[0230] where P Bmax is the maximum charge and discharge power of the power battery.

[0231] Subsequently, according to the internal coupling relationship of the controlled variables of the mine car drive system, the equality constraint relationship of the controlled variables in the preliminary optimization objective function of the mine car drive system in the acceleration stage in the current control period k is obtained, which is expressed by the following formula:

[0232]

[0233] (2-1-3) Convert the equality constraint relationship and the inequality constraint relationship of the controlled variables in the preliminary optimization objective function of the mine car drive system in the acceleration stage in the current control period k obtained in step (2-1-2) respectively, so as to obtain the penalty function corresponding to the equality constraint relationship and the barrier function B x corresponding to the inequality constraint relationship.

[0234] Specifically, in this step (2-1-3), first, the penalty function

[0235]

[0236] is obtained according to the equality constraint relationship, where C5 is the equality constraint penalty coefficient, and its range is [0,1], preferably 0.4; a Gj (j = 1, 2) is the fitting value of the generator output power and the engine speed; a Gj (j = 3, 4, 5) is the fitting value of the generator output power and the generator excitation current, and this 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.

[0237] Then, logarithmic barrier transformation is performed on the inequality constraint relationship to obtain the corresponding barrier function B x :

[0238] B x =-C x lg(x max -x)

[0239] where the value range 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;

[0240] 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

[0241] Then, the inequality constraint relationship is transformed into a quadratic logarithmic barrier to obtain the corresponding barrier function

[0242]

[0243] 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:

[0244] (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

[0245] Specifically, the comprehensive optimization objective mathematical model of the mine car drive system in the acceleration stage in the current control period k is represented by the following formula:

[0246]

[0247] where x is the controlled variable, and are the input powers of the left braking resistor and the right braking resistor in the current control period k;

[0248] The advantage of this step is to establish the loss mathematical model, penalty mathematical model, and barrier function mathematical model of the system in operation of the mine car drive system in the acceleration stage in the current control period k to obtain the comprehensive optimization objective mathematical model of the mine car drive system in the acceleration stage in the current control period k. The coupling between each controlled variable is comprehensively considered, and at the same time, the constraint conditions of the controlled quantity are transformed into the corresponding barrier function, so that in the process of optimizing the control target, the boundary of the controlled quantity is automatically avoided.

[0249] (2-2) Obtain the comprehensive optimization objective mathematical model of the mine car drive system in the deceleration stage in the current control period k according to the reference angular velocities of the left drive wheel and the right drive wheel in the next control period k+1 obtained in step (1-7) Then the process ends.

[0250] This step specifically includes the following sub-steps:

[0251] (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 velocities of the left drive wheel and the right drive wheel and the reference angular velocities in the next control period k+1 according to the operating parameters and the reference angular velocities of the left drive wheel and the right drive wheel 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 .

[0252] The operating parameters of the mine car drive system in the deceleration stage include: the moments of inertia J of the left drive wheel and the right drive wheel of the mine car drive system, the control period duration t of the mine car drive system, the input power of the braking resistor in the current control period k the angular velocities of the left drive wheel and the right drive wheel in the current control period k and the driving torques of the left drive wheel and the right drive wheel in the current control period k and the load torques of the left drive wheel and the right drive wheel in the current control period k and Updated angular velocity values of the left and right drive wheels in the next control period k+1 and Reference angular velocity difference penalty coefficient C1, slip angular velocity difference penalty coefficient C2, oil price C3 of the fuel used by the engine (referred to as the engine) of the mine car drive system, and rotational speed n corresponding to the minimum fuel consumption of the engine min , charge and discharge loss coefficient c4 of the power battery (referred to as the power battery) in the mine car drive system, rotational speed n of the engine in the current control period k k , and charge and discharge power of the power battery in the current control period k

[0253] Specifically, in this step (2-2-1), first, according to the moment of inertia J of the left and right drive wheels, the control period duration t of the mine car drive system, the load torque of the left and right drive wheels in the current control period k and angular velocity of the left and right drive wheels in the current control period k and drive torque of the left and right drive wheels in the current control period k and input power of the left and right braking resistors in the current control period k and respectively obtain the updated angular velocity values of the left and right drive wheels of the mine car drive system in the deceleration stage in the next control period k+1 and

[0254]

[0255] Subsequently, obtain the penalty value of the reference angular velocity difference in the current control period k according to the formula in step (2-1-1) penalty value of the slip angular velocity difference in the current control period k loss value of the engine in the current control period k and loss value of the power battery in the current control period k

[0256] Thereafter, sum up the penalty value of the reference angular velocity difference in the current control period k obtained in the above steps penalty value of the slip angular velocity difference in the current control period k loss value of the engine in the current control period k and loss value of the power battery in the current control period k to obtain the preliminary optimization objective function of the mine car drive system in the deceleration stage in the current control period k

[0257] (2-2-2) Obtain the barrier 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 for the mine car drive system in the deceleration stage obtained in step (2-2-1) in the current control period k.

[0258] Specifically, in this step (2-2-2), 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 (the upper and lower boundary values include: the maximum power consumption P of the left and right drive wheels motmax , the maximum excitation current I of the generator max , the maximum output power P of the generator Gmax , the maximum recovery power P of the kinetic energy recovery system regmax , the maximum charge and discharge power P of the power battery Bmax , the maximum input power P of the braking resistor Rmax ), determine the inequality constraint relationship of the controlled quantity in the preliminary optimization objective function:

[0259] The inequality constraint relationship of the controlled quantity in the preliminary optimization objective function specifically includes the following inequality constraints:

[0260] A. Inequality constraint on the kinetic energy recovery power of the left and right drive wheels in the current control period k:

[0261]

[0262] Where and are the kinetic energy recovery powers of the left and right drive wheels in the current control period k; P regmax is the maximum kinetic energy recovery power of the left and right drive wheels. τ is the electromechanical conversion efficiency of the left and right drive wheels.

[0263] B. Inequality constraint relationship on the rotational speed of the engine in the current control period k:

[0264] 0 ≤ n k ≤ n max

[0265] C. Inequality constraint relationships on the excitation current of the generator and the output power of the generator in the current control period k:

[0266] 0 ≤ I k ≤ I max

[0267]

[0268] D. Inequality constraint relationship of charge and discharge power of power battery in current control period k:

[0269]

[0270] E. The input power inequality constraint of the left braking resistor and the right braking resistor in the current control period k is expressed by the following formula:

[0271]

[0272] where P Rmax is the maximum input power of the braking resistor.

[0273] Subsequently, according to the inherent coupling relationship of the controlled variables of the mine car drive system, the equality constraint relationship of the controlled variables in the preliminary optimization objective function of the mine car drive system in the deceleration stage in the current control period k is obtained:

[0274]

[0275] (2-2-3) Transform the inequality constraint relationship and the equality constraint relationship of the controlled variables 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 period k to obtain the penalty function corresponding to the equality constraint relationship and the barrier function B corresponding to the inequality constraint relationship x .

[0276] Specifically, in this step (2-2-3), first, according to the above equality constraint relationship, the penalty function corresponding to the equality constraint relationship can be obtained

[0277]

[0278] Then, perform logarithmic barrier transformation on the inequality constraint relationship to obtain the corresponding barrier function B z :

[0279] B z =-C z lg(z max -z)

[0280] where z takes values of n k , I k , and C z is the weight coefficient, and its range is [0,1]. The smaller C x , the closer the optimization result of x is to x max . and are preferably 0.3; and is preferably 0.6.

[0281] Subsequently, in the same manner as in step (2-1-3), the inequality constraint relationship is subjected to a quadratic logarithmic barrier transformation to obtain the corresponding barrier function

[0282]

[0283] (2-2-4) Sum up the penalty function and the barrier function obtained in step (2-2-3) and the preliminary optimization objective function of the mine car drive system in the deceleration stage obtained in step (2-2-1) in the current control cycle k to obtain the comprehensive optimization objective mathematical model of the mine car drive system in the deceleration stage in the current control cycle k

[0284] Specifically, the comprehensive optimization objective mathematical model of the mine car drive system in the deceleration stage in the current control cycle k is represented by the following formula:

[0285]

[0286]

[0287] where x is the controlled variable,

[0288] The advantage of this step is to establish the loss mathematical model, penalty mathematical model, and barrier function mathematical model of the mine car drive system in the deceleration stage in the current control cycle k to obtain the comprehensive optimization objective mathematical model of the mine car drive system in the deceleration stage in the current control cycle k The mathematical model of the braking resistor and the angular velocity is established, making the braking control more precise.

[0289] (3) Solve the comprehensive optimization objective mathematical models of the mine car drive system in the acceleration stage and the deceleration stage obtained in step (2) in the current control cycle k through the Slime Mould Algorithm (SMA) to obtain the optimal solution.

[0290] As Figure 2 shown, this step includes the following sub-steps:

[0291] (3-1) Initialize the parameters of the SMA algorithm.

[0292] Specifically, this step includes: initializing the number of slime moulds Pop = 20; initializing the random search probability z ra = 0.2; initializing the maximum number of iterations Cmax = 50; Initialize the lower bound Bd of the search space low = 0; Initialize the upper bound 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 n0I0P 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 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 output power of the power battery; P RR0 is the initial value of the power input of the right braking resistor; P RL0 is the initial value of the power input of the left braking resistor, Initialize the SMA iteration count k SMA to 1; Randomly generate the position of the d-th slime mold in the search space at the k SMA -th iteration as the slime mold position corresponding to this slime mold, d ∈ [1, Pop]; Generate a random number for the randomly generated d-th slime mold, and the range of the random number is [0, 1], as the random weight change rate r d .

[0293] (3 - 2) Judge whether the SMA iteration count k SMA in the parameters of the SMA algorithm after initialization in step (3 - 1) is greater than the maximum iteration count C max , if so, go to step (3 - 9), otherwise go to step (3 - 3);

[0294] (3 - 3) According to the parameters of the SMA algorithm after initialization 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 obtained in step (2) in the current control cycle k, obtain the food concentration value of each slime mold in the SMA algorithm at the k SMA -th iteration

[0295] Specifically, this step calculates the food concentration value by judging whether the mine car drive system is in the deceleration stage If so, there is:

[0296]

[0297] If in the acceleration stage, there is:

[0298]

[0299] wherein is the food concentration of the d-th slime mold at the k-th SMA iteration; is the position of the d-th slime mold in the search space at the k-th SMA iteration, where d ∈ [1, Pop].

[0300] (3-4) Sort the food concentration values of all the slime molds obtained in step (3-3) at the k-th SMA iteration to obtain the highest food concentration value at the k-th iteration SMA and its corresponding slime mold position (i.e., after determining the highest food concentration value , the corresponding slime mold can be determined, and thus the corresponding slime mold can be determined), as well as the lowest food concentration value

[0301] (3-5) For the d-th slime mold in the SMA algorithm, based on the random change rate r of the weight of this slime mold obtained in step (3-1) d and the highest and lowest food concentration values obtained in step (3-4), obtain the weight of this slime mold at the k-th SMA iteration SMA

[0302] Specifically, the new slime mold weight is expressed by the following formula

[0303]

[0304] wherein is the highest food concentration value of all the slime molds at the k-th SMA iteration; is the lowest food concentration value of all the slime molds at the k-th SMA iteration; is the food concentration value of the d-th slime mold at the k-th SMA iteration.

[0305] (3-6) For the d-th slime mold in the SMA algorithm, obtain the position update value of this slime mold at the k-th SMA iteration based on the highest food concentration value obtained in step (3-4)

[0306] Specifically, the position update value of the slime mold at the k-th SMA iteration is expressed by the following formula:

[0307]

[0308] Among them is the position update value of the slime mold at the k SMA th time.

[0309] (3-7) Randomly select two slime molds α and β from all the 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, according to the randomly changing weight rate r d of the slime mold obtained in step (3-1), the upper boundary Bd up of the search space, the lower boundary Bd low of the search space, the random search probability z ra , the slime mold position corresponding to the highest food concentration value obtained in step (3-4) the slime mold weight obtained in step (3-5) the position update value obtained in step (3-6) obtain the slime mold position of this slime mold at the k SMA +1th iteration

[0310] Specifically, this step uses the following formula to obtain the slime mold position of this slime mold at the k SMA +1th iteration

[0311]

[0312] where z ra is the random search probability, and 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 is the self-preservation weight, which is a random number, and its range is obtained by a random number generator.

[0313] (3-8) Set k SMA = k SMA +1, and return to step (3-2).

[0314] (3-9) Sort the food concentration values of all slime molds at the k SMA th iteration obtained in step (3-3) in descending order, and take the first food concentration values and their corresponding slime mold positions as the optimal solutions.

[0315] (4) According to the optimal solution obtained in step (3), the improved PSO algorithm is used to obtain the final optimal solution, which is used as the output signal of the drive system to control the mine car drive system.

[0316] As Figure 3 shown, this step includes the following sub-steps:

[0317] (4-1) Initialize the parameters of the particle swarm algorithm according to the optimal solution obtained in step (3-9) to obtain the initialized parameters.

[0318] Specifically, this step includes: initializing the number of particle swarms N PSO = Pop; initializing the lower boundary of the velocity of each particle where e u is the unit vector corresponding to each component of the velocity of each particle; initializing the upper boundary of the velocity of each particle as Initializing the lower boundary of the position of each particle x low = 0; initializing the upper boundary of the position of each particle Initializing the maximum number of iterations S max as 20; generating a particle at each slime mold position among the slime mold positions obtained in step (3-9), and initializing the velocity of each particle to 0, initializing the PSO iteration number k PSO = 1.

[0319] (4-2) Judge whether the PSO iteration number k PSO is greater than the maximum number of iterations S max , if so, go to step (4-9), otherwise go to step (4-3).

[0320] (4-3) Obtain the fitness of each particle in the PSO algorithm at the k PSO th iteration according to the food concentration value of the dth slime mold in the SMA algorithm obtained in step (3-3)

[0321] Specifically, if the mine car drive system is in the deceleration stage, then:

[0322]

[0323] If the mine car drive system is in the acceleration stage, then:

[0324]

[0325] where p ∈ [1, N PSO .

[0326] (4-4) For the fitness of each particle in the PSO algorithm obtained in step (4-3) at the k PSO -th iteration Obtain the optimal fitness of the entire particle swarm and its corresponding position

[0327] Specifically, for the fitness of all particles obtained in step (4-3) at the k PSO -th iteration, take the position of the particle corresponding to the lowest fitness as the position corresponding to the optimal fitness of the entire particle swarm and take this lowest fitness as the optimal fitness L of the entire particle swarm b .

[0328] (4-5) Judge whether the fitness of the p-th particle in the PSO algorithm at the k PSO -th iteration is less than the fitness at the k PSO -1-th iteration. If so, set the position at the k PSO -th iteration as the position corresponding to the personal best value of the p-th particle at the k PSO -th iteration, and then enter step (4-6); otherwise, set the position at the k -1-th iteration as the position corresponding to the personal best value of the p-th particle at the k PSO -th iteration PSO and then enter step (4-6).

[0329] Through the above method, obtain the position corresponding to the personal best value of each particle at the k PSO -th iteration

[0330] (4-6) According to the fitness of each particle obtained in step (4-3) and the optimal fitness of the entire particle swarm obtained in step (4-4) obtain the compression factor of the p-th particle at the k PSO -th iteration the personal learning factor of the p-th particle at the k PSO -th iterationand the swarm learning factor of the p-th particle at the k PSO -th iteration

[0331] Specifically, this step first obtains the personal learning factor of the p-th particle at the k PSO -th iteration and the swarm learning factor of the p-th particle at the k PSO -th iteration ​

[0332]

[0333] Then, calculate the compression factor of the p-th particle at the k-th PSO iteration through the following formula

[0334]

[0335] where is the auxiliary factor of the p-th particle at the k-th PSO iteration and is equal to:

[0336]

[0337] (4 - 7) According to the individual learning factor of the p-th particle at the k-th PSO iteration calculated in (4 - 6) The group learning factor of the p-th particle at the k-th PSO iteration The compression factor of the p-th particle at the k-th PSO iteration Obtain the position and velocity of the p-th particle at the (k + 1)-th PSO iteration

[0338] Specifically, in this step, first, calculate the position of the p-th particle at the (k + 1)-th PSO iteration and the velocity of the p-th particle at the (k + 1)-th PSO iteration which is represented by the following formula:

[0339]

[0340] where r1 and r2 are random numbers, both in the range [0, 1], obtained from a random number generator;

[0341]

[0342] where is the updated velocity of the p-th particle at the (k + 1)-th PSO iteration; is the velocity of the p-th particle at the k-th PSO iteration; is the position of the p-th particle at the k-th PSO iteration.

[0343] Subsequently, calculate the position of the p-th particle at the (k + 1)-th PSO iteration

[0344]

[0345] (4 - 8) Set k PSO = k PSO + 1, and return to step (4 - 2);

[0346] (4 - 9) Take the position corresponding to the optimal fitness of the entire particle swarm obtained as the final optimal solution, and use it as the output signal of the drive system to control each subsystem of the mine car drive system.

[0347] The advantages of steps (4 - 1) to (4 - 9) are that the initial positions of the particle swarm are set near the optimal solution and the sub - optimal solution of the previous step, and the number of particles at different positions decreases as the food concentration value decreases, which improves the deep - development ability of the particle swarm for the optimal solution of the previous step. At the same time, it also conducts development near the sub - optimal solution, avoiding the particle swarm algorithm from converging to a local optimal solution; meanwhile, 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.

[0348] The advantages of steps (3 - 1) to (4 - 9) are that they utilize the characteristics of strong global development ability of SMA and the fast convergence speed of PSO after introducing a compression factor, which reduces the probability of falling into a local optimal solution during the optimization process and improves the accuracy of the optimization result.

[0349] Those skilled in the art can easily understand that the above - mentioned are only the preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall 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 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 drive system, and the reference angular velocity of the left drive wheel and the right drive wheel in the current control cycle k; (2) obtaining 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 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); (3) solving the comprehensive optimization target mathematical model of the mine car drive system in the acceleration stage and the deceleration stage obtained in step (2) in the current control cycle k by using the slime mold algorithm SMA to obtain the optimal solution; (4) According to the optimal solution obtained in step (3), the improved PSO algorithm is used to obtain the final optimal solution, and it 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 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 : 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 total pedal opening value obtained 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 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 driving system according to claim 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) Obtaining the inequality constraint relationship and the equality constraint relationship of the controlled quantity in 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); (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]; 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]; 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]; when When The value of represents the power battery discharge power; When The value represents the power battery charging power; 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 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]; a Gj (j=1,2) is the fitting value of generator output power and engine speed; a Gj (j=3, 4, 5) are the fitting values ​​of the generator output power and the generator excitation current. The coefficients are 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. 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 ; 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]; C y The smaller it is, the closer the optimization result of y is to y max ; 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, and is the input power of the left brake resistor and the right brake resistor in the current control cycle k.

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 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: Where P Rmax The maximum input power of the brake 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 : 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 ; 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 the 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 the times; (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 by 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 optimal 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 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,N 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 during iteration; Then, calculate the p-th particle at the k-th PSO +1 position at iteration

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