Distributed driving vehicle driving stability control method

Abstract

The invention discloses a distributed driving vehicle running stability control method, which comprises the following steps of: establishing a single-point preview deviation model, and calculating a front wheel turning angle of a vehicle; designing a rear wheel steering angle sliding mode controller, and eliminating the deviation between the actual vehicle side slip angle and the ideal side slip angle; an additional yaw moment sliding mode controller is designed, and the deviation between the actual yaw velocity and the ideal yaw velocity of the vehicle is eliminated; a longitudinal vehicle speed PID controller is designed according to the deviation between the actual vehicle speed and the ideal vehicle speed; an improved whale optimization algorithm is determined, and parameter self-tuning is carried out on the sliding mode controller; according to the Ackerman steering principle, the dynamic loads of the front axle and the rear axle of the vehicle redistribute the turning angle and the torque of the vehicle. Aiming at the problem of poor control caused by multi-parameter setting of the sliding mode controller, the improved whale optimization algorithm is introduced and is used for parameter setting of the sliding mode controller, and the stability performance of a common sliding mode control method and the adaptability of the common sliding mode control method to different driving working conditions can be improved.

Description

Technical Field

[0001] The present invention belongs to the field of automatic driving, and in particular is a method for controlling the driving stability of a distributed drive vehicle. Background Art

[0002] The independent controllability of each wheel of a distributed drive vehicle enables it to have low-speed maneuverability and high-speed stability, making it an ideal carrier for intelligent vehicles. However, excessive angle / torque control inputs greatly increase the control difficulty, making it difficult to ensure driving stability.

[0003] At present, stability control algorithms include linear control and nonlinear control. The main method is PID control, but it cannot handle external disturbances and cannot guarantee stability in a large range. The MPC control method can effectively handle multiple constraints of the system, but its real-time performance is poor and the calculation speed needs to be improved. The sliding mode control system has strong anti-interference ability and fast response speed. It is a highly reliable and effective control method. Its unique design enables the system to stably control the target variables in a complex environment without being affected by external interference, but its performance is affected by multiple parameters. Therefore, parameter setting is a major problem for sliding mode control. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a distributed drive vehicle driving stability control method in view of the deficiencies of the above-mentioned prior art. This control method aims at the problem of poor control caused by multi-parameter setting of the sliding mode controller. By introducing an improved whale optimization algorithm for sliding mode controller parameter setting, the stability performance of the general sliding mode control method and its adaptability to different driving conditions can be improved.

[0005] In order to achieve the above technical objectives, the technical solution adopted by the present invention is:

[0006] A distributed drive vehicle driving stability control method comprises the following steps:

[0007] Step S1: Based on the constant yaw rate assumption and in combination with the mathematical relationship of vehicle steering, a single-point preview deviation model is established to calculate the front wheel steering angle of the vehicle;

[0008] Step S2: Design a rear wheel steering angle sliding mode controller to eliminate the deviation between the actual vehicle center of mass sideslip angle and the ideal center of mass sideslip angle;

[0009] Step S3: designing an additional yaw moment sliding mode controller to eliminate the deviation between the actual vehicle yaw rate and the ideal yaw rate;

[0010] Step S4: designing a longitudinal vehicle speed PID controller according to the deviation between the actual vehicle speed and the ideal vehicle speed;

[0011] Step S5: determining an improved whale optimization algorithm by initializing the chaotic map, nonlinearly updating the convergence factor, and adjusting the position of the adaptive inertia weight, and performing parameter self-tuning on the sliding mode controllers of step S2 and step S3 according to the requirements of the fitness function;

[0012] Step S6: Redistribute the vehicle steering angle and torque respectively according to the Ackerman steering principle and the dynamic loads of the front and rear axles of the vehicle.

[0013] As a further improved technical solution of the present invention, in step S1, based on the constant yaw rate assumption and combined with the mathematical relationship of vehicle steering, a single-point preview deviation model is established, specifically:

[0014] Assume that the vehicle maintains a constant yaw rate for a period of time in the future, and the lateral speed v of the vehicle y Much smaller than the longitudinal speed v x , determine the combined speed of the vehicle The value remains unchanged;

[0015] Based on this constant yaw rate assumption, the ideal yaw rate can be obtained:

[0016]

[0017] Where, ΔY is the lateral deviation between the preview point and the vehicle, t p Seconds is the preview time, β is the vehicle's center of mass side slip angle;

[0018] Combined with the mathematical relationship of vehicle steering, the front wheel turning angle of the vehicle can be obtained as:

[0019]

[0020] Among them, h is the wheelbase of the front and rear axles of the vehicle, and K is the stability factor.

[0021] As a further improved technical solution of the present invention, step S2 specifically includes:

[0022] S201: Side slip angle deviation e β As the sliding mode control variable e1, the rear wheel angle δ r As the output variable u1, that is, e1 = e β =β-β d , u1=δ r , the integral sliding surface is designed as:

[0023] Where β is the actual side slip angle of the vehicle's center of mass; β d is the ideal center of mass sideslip angle; are the proportional and integral coefficients respectively, both are non-zero real numbers;

[0024] S202: Based on the traditional exponential convergence law Based on the continuous curve, the hysteresis of sgn(s) is overcome and the continuous curve is designed as follows:

[0025]

[0026] Among them, θ is a non-zero real number;

[0027] The approach rate is designed to be

[0028] Among them, s1 is the sliding surface; ε1 and K1 are non-zero coefficients;

[0029] S203: Combine the two-degree-of-freedom vehicle model, and solve the equation e1=e β =β-β d Taking the derivative of both sides, we get:

[0030]

[0031] Where, m is the vehicle mass; l f is the distance from the front axle to the center of mass, l r is the distance from the rear axle to the center of mass; C f is the front wheel cornering stiffness; C r is the rear wheel cornering stiffness; γ is the actual yaw rate;

[0032] Substituting the approach rate into the sliding surface, we get:

[0033]

[0034] Combining equations (5) and (6) we can get the rear wheel turning angle formula:

[0035]

[0036] Among them, δ r is the rear wheel turning angle.

[0037] As a further improved technical solution of the present invention, step S3 specifically includes:

[0038] S301: Compare the actual yaw rate γ with the ideal yaw rate γ d The deviation e γ As the sliding mode control variable e2, the additional yaw moment ΔM is used as the output variable u2, that is, e2 = e γ =γ-γ d , u2=ΔM, the integral sliding surface is designed as are the proportional coefficient and the integral coefficient respectively, both are non-zero real numbers;

[0039] S302: The approach rate is designed to be

[0040] Among them, s2 is the sliding surface; ε2 and K2 are non-zero coefficients;

[0041] S303: Combine the two-degree-of-freedom vehicle model, and calculate the equation e2=e γ =γ-γ d Taking the derivative of both sides, we get:

[0042]

[0043] Among them, I z is the moment of inertia of the vehicle's center of mass about the axis perpendicular to the ground;

[0044] Substituting the approach rate into the sliding surface, we get:

[0045]

[0046] Combining equations (9) and (10), we can get the additional yaw moment formula:

[0047]

[0048] As a further improved technical solution of the present invention, step S4 specifically includes:

[0049] S401, calculate speed deviation e v :

[0050] e v =v x_t -v x (12);

[0051] In the formula, v x_t is the target vehicle speed, v x is the actual vehicle speed;

[0052] S402, calculating the longitudinal moment of the vehicle:

[0053]

[0054] In the formula, k p , k i , k d They represent proportional, integral, and differential constants respectively.

[0055] As a further improved technical solution of the present invention, step S5 specifically includes:

[0056] S501: Input, output and fitness function: An improved whale optimization algorithm input includes: lateral deviation index J1, speed deviation index J2, yaw rate index J3 and center of mass sideslip angle index J4, population size and number of iterations; output includes rear wheel angle sliding mode controller parameters ε1, K1 and additional yaw moment sliding mode controller parameters ε2, K2; the fitness function is:

[0057]

[0058] Where c1, c2, c3 and c4 are non-zero weight coefficients, e d (t′) is the lateral deviation at time t′ within a T period, e v (t′) represents the velocity deviation at time t′ within a T cycle, γ(t′) is the yaw rate at time t′ within a T cycle, and β(t′) is the sideslip angle of the center of mass at time t′ within a T cycle;

[0059] S502: Population chaos initialization design: Use Chebyshev chaotic sequence to initialize the position of the whale population, specifically:

[0060] Assume that the chaotic sequence generated in the D-dimensional Euclidean search space is expressed as:

[0061]

[0062] The mathematical expression for generating the Chebyshev chaotic map is:

[0063] y i+1,d = cos(χ·cos -1 y id ),y id ∈[-1,1](16);

[0064] Where χ is the order of the Chebyshev map; when used, a set of pseudo-random numbers y is generated arbitrarily within a given range. 1d ={y 1d ,d=1,2,…,D}, according to formula (15), we can get the uniformly distributed whole set of random number sequence y N×D , which is the Chebyshev chaotic sequence;

[0065] Mapping the chaotic sequence obtained by formula (16) into the algorithm solution space, the initialized whale population is obtained as:

[0066]

[0067] Among them, X che For the entire whale population; X i che is the i-th whale individual; X id che is the dth dimension where the i-th whale individual is located; X d max represents the i-th whale individual Xi che The upper limit of the value of the dth dimension, X d min represents the i-th whale individual X i che The lower limit of the value of the dth dimension;

[0068] S503: Nonlinear Convergence Factor Design:

[0069] The nonlinear time-varying update strategy is used to update the convergence factor a:

[0070]

[0071] In the formula, n and m are constants; t is the number of iterations; t max is the maximum number of iterations;

[0072] S504: Adaptive inertia weight position adjustment design:

[0073] Setting whale individuals The fitness is f i (t), the average fitness of the population is f min (t) is the optimal fitness of the current population. The population is divided into two subgroups based on the numerical relationship of fitness, and different inertia weights are used to adjust the positions of individuals. The inertia weight is designed as follows:

[0074]

[0075] Among them, w i (t) represents the inertia weight of the i-th whale individual in the t-th iteration; w max Indicates the upper limit of the inertia weight, w min Indicates the lower limit of the inertia weight;

[0076] S505: Main loop: An improved whale optimization algorithm performs N iterations, each iteration includes the following steps:

[0077] a. Population chaos initialization;

[0078] b. Call the simulation module;

[0079] c. Record and calculate the optimal individual fitness value and position in the current population;

[0080] Among them, the optimal fitness represents the minimum fitness value, expressed as f min (t) indicates;

[0081] d. Update the nonlinear convergence factor;

[0082] e. Update parameters A, C, l;

[0083] Among them, A and C are coefficient vectors in the whale optimization algorithm; l is a random number between [-1,1];

[0084] f. Calculate the average fitness value of the population and the corresponding inertia weight;

[0085] The average fitness of the population is

[0086] g. Update the individual position according to the probability value p and the position parameter A;

[0087] Introducing the above improved method (i.e., inertia weight) into the individual position update formula can be obtained:

[0088]

[0089] Among them, X best (t) is the optimal position vector of the current iteration; D is the distance vector between the optimal position and the individual position; A is the coefficient variable; X rand (t) represents the randomly selected whale position in the tth iteration; b is the logarithmic spiral shape constant; l is a random number between [-1,1];

[0090] S506: After N iterations, output the optimal rear wheel steering angle sliding mode controller parameters K1, ε1 and additional yaw moment sliding mode controller parameters K2, ε2.

[0091] As a further improved technical solution of the present invention, step S6 specifically includes:

[0092] S601: The turning angle distributor receives the front wheel turning angle δ of the single-point preview controller f and the rear wheel steering angle δ of the sliding mode controller r , and redistributed according to the following Ackerman rotation relationship:

[0093]

[0094] In the formula, δ fl is the left front wheel turning angle; δ fr is the right front wheel turning angle; δ rl is the left rear wheel turning angle; δ rr is the right rear wheel turning angle; B f is the distance between the front wheels; B r is the distance between the rear wheels;

[0095] S602: The steering angle distributor receives the additional yaw moment ΔM and the longitudinal force F t, and redistribute according to the following estimated dynamic load relationship between the front and rear axles:

[0096]

[0097] Where, T fl is the left front wheel torque; T fr is the right front wheel torque; T rl is the left rear wheel torque; T rr is the torque of the right rear wheel; R is the wheel radius; F z_fl is the vertical load on the left front wheel; F z_fr is the vertical load on the right front wheel; F z_rl is the vertical load on the left rear wheel; F z_rr is the vertical load on the right rear wheel.

[0098] As a further improved technical solution of the present invention, the two-degree-of-freedom vehicle model formula in step S203 is:

[0099]

[0100] Where m is the vehicle mass; l f is the distance from the front axle to the center of mass; l r is the distance from the rear axle to the center of mass; v is the speed at the center of mass; I z C is the moment of inertia of the vehicle's center of mass around the vertical ground axis; f is the front wheel cornering stiffness; C r is the rear wheel cornering stiffness.

[0101] The beneficial effects of the present invention are:

[0102] In order to solve the problem of poor control caused by multi-parameter setting of the sliding mode controller, the present invention introduces an improved whale optimization algorithm, designs a fitness function that can maintain the driving stability of the vehicle, and uses it for parameter setting of the sliding mode controller, thereby effectively improving the stability of the vehicle under different driving conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0103] Figure 1 It is a schematic diagram of a distributed drive vehicle driving stability control method of the present invention.

[0104] Figure 2 Iteration graph for the improved whale optimization algorithm.

[0105] Figure 3 Schematic diagram of the Ackerman turn.

[0106] Figure 4 This is a comparison of the low-adhesion road conditions under different control methods.

[0107] Figure 5This is a comparison of the high-adhesion road conditions under different control methods. DETAILED DESCRIPTION

[0108] The specific embodiments of the present invention are further described below according to the accompanying drawings:

[0109] The independent controllable feature of each wheel of the 4WIS-4WID vehicle enables it to have low-speed maneuverability and high-speed stability, making it an ideal carrier of intelligent vehicles. However, too much angle / torque control input greatly increases the control difficulty, making it difficult to balance driving stability and path tracking accuracy. For this reason, this embodiment proposes a hierarchical control strategy based on improved whale algorithm optimization, including a path tracking layer, a stability control layer, and a torque angle distribution layer. Based on the single-point preview deviation model, a front-wheel steering controller is designed to achieve path tracking control. Combining the two-degree-of-freedom vehicle model and sliding mode control theory, a sliding mode controller with integrated control of rear-wheel steering and direct yaw moment is designed to eliminate the influence of the sideslip angle and yaw rate on vehicle stability. A new approach rate is introduced to eliminate the chattering problem of sliding mode control. At the same time, dynamic load transfer and Ackerman steering theorem are used to distribute the wheel torque and angle. In order to solve the problem of poor control caused by multi-parameter tuning of sliding mode controller, an improved whale optimization algorithm (IWOA) is obtained through three methods: chaos map initialization, nonlinear update of convergence factor, and adaptive inertia weight position adjustment. A fitness function that takes into account both vehicle driving stability and path tracking accuracy is designed and used for sliding mode controller parameter tuning. Simulation experiments show that compared with front-wheel steering vehicles and 4WIS-4WID vehicles, the 4WIS-4WID intelligent vehicle optimized based on the improved whale algorithm has the best comprehensive performance, which greatly improves driving stability while ensuring path tracking accuracy.

[0110] like Figure 1 As shown, a distributed drive vehicle driving stability control method integrating an improved whale optimization algorithm comprises the following steps:

[0111] Step S1: Based on the constant yaw rate assumption and combined with the mathematical relationship of vehicle steering, a single-point preview deviation model is established to calculate the vehicle front wheel steering angle.

[0112] Among them, based on the constant yaw rate assumption and combined with the mathematical relationship of vehicle steering, a single-point preview deviation model is established, specifically:

[0113] Assume that the vehicle maintains a constant yaw rate for a period of time in the future, and the lateral speed v of the vehicle y Much smaller than the longitudinal speed v x , determine the combined speed of the vehicle The value remains unchanged.

[0114] Based on this constant yaw rate assumption, the ideal yaw rate can be obtained:

[0115]

[0116] Where, ΔY is the lateral deviation between the preview point and the vehicle, t p Seconds is the preview time, β is the sideslip angle of the vehicle's center of mass.

[0117] Combined with the mathematical relationship of vehicle steering, the front wheel turning angle of the vehicle can be obtained as:

[0118]

[0119] Among them, h is the wheelbase of the front and rear axles of the vehicle, and K is the stability factor.

[0120] Step S2: Design a rear wheel steering angle sliding mode controller to eliminate the deviation between the actual vehicle center of mass sideslip angle and the ideal center of mass sideslip angle.

[0121] Step S2 is specifically as follows:

[0122] S201: Side slip angle deviation e β As the sliding mode control variable e1, the rear wheel angle δ r As the output variable u1, that is, e1 = e β =β-β d , u1=δ r , the integral sliding surface is designed as: Where β is the actual side slip angle of the vehicle's center of mass; β d is the ideal center of mass sideslip angle; are the proportional and integral coefficients respectively, both are non-zero real numbers.

[0123] S202: Based on the traditional exponential convergence law Based on the continuous curve, the hysteresis of sgn(s) is overcome and the continuous curve is designed as follows:

[0124]

[0125] Among them, θ is a non-zero real number, s is the sliding surface;

[0126] The approach rate is designed to be

[0127] Among them, s1 is the sliding surface; ε1 and K1 are non-zero coefficients.

[0128] S203: The two-degree-of-freedom vehicle model formula is:

[0129]

[0130] Where m is the vehicle mass; l fis the distance from the front axle to the center of mass; l r is the distance from the rear axle to the center of mass; v is the speed at the center of mass; I z C is the moment of inertia of the vehicle's center of mass around the vertical ground axis; f is the front wheel cornering stiffness; C r is the rear wheel cornering stiffness.

[0131] Combined with the above two-degree-of-freedom vehicle model, the equation e1=e β =β-β d Taking the derivative of both sides, we get:

[0132]

[0133] Where γ is the actual yaw rate.

[0134] Substituting the approach rate into the sliding surface, we get:

[0135]

[0136] Combining equations (5) and (6) we can get the rear wheel turning angle formula:

[0137]

[0138] Among them, δ r is the rear wheel turning angle.

[0139] Step S3: Designing an additional yaw moment sliding mode controller to eliminate the deviation between the actual vehicle yaw rate and the ideal yaw rate; specifically including:

[0140] S301: Compare the actual yaw rate γ with the ideal yaw rate γ d The deviation e γ As the sliding mode control variable e2, the additional yaw moment ΔM is used as the output variable u2, that is, e2 = e γ =γ-γ d , u2=ΔM, the integral sliding surface is designed as are the proportional coefficient and the integral coefficient respectively, both are non-zero real numbers.

[0141] S302: The approach rate is designed to be

[0142] Among them, s2 is the sliding surface; ε2 and K2 are non-zero coefficients.

[0143] S303: Combine the two-degree-of-freedom vehicle model, and calculate the equation e2=e γ =γ-γ d Taking the derivative of both sides, we get:

[0144]

[0145] Among them, Iz is the moment of inertia of the vehicle's center of mass about the axis perpendicular to the ground;

[0146] Substituting the approach rate into the sliding surface, we get:

[0147]

[0148] Combining equations (9) and (10), we can get the additional yaw moment formula:

[0149]

[0150] Step S4: According to the deviation e between the actual vehicle speed and the ideal vehicle speed v Design a longitudinal vehicle speed PID controller; specifically include:

[0151] S401, calculate speed deviation e v :

[0152] e v =v x_t -v x (12);

[0153] In the formula, v x_t is the target vehicle speed, i.e. the ideal vehicle speed, v x is the actual vehicle speed;

[0154] S402, calculating the longitudinal moment of the vehicle:

[0155]

[0156] In the formula, k p , k i , k d They represent proportional, integral, and differential constants respectively.

[0157] Step S5: determining an improved whale optimization algorithm by initializing the chaotic map, nonlinearly updating the convergence factor, and adjusting the position of the adaptive inertia weight, and performing parameter self-tuning on the sliding mode controller of step S2 and step S3 according to the requirements of the fitness function; the step S5 specifically includes:

[0158] S501: Input, output and fitness function: An improved whale optimization algorithm includes the input of lateral deviation index J1, speed deviation index J2, yaw rate index J3 and center of mass sideslip angle index J4, population size and iteration number; output includes rear wheel steering angle sliding mode controller parameters (such as ε1, K1) and additional yaw moment sliding mode controller parameters (such as ε2, K2); the fitness function is:

[0159]

[0160] Where c1, c2, c3 and c4 are non-zero weight coefficients, e d (t′) is the lateral deviation at time t′ within a T period, e v (t′) represents the velocity deviation at time t′ within a T cycle, γ(t′) is the yaw rate at time t′ within a T cycle, and β(t′) is the sideslip angle of the center of mass at time t′ within a T cycle.

[0161] S502: Population chaos initialization design: Use Chebyshev chaotic sequence to initialize the position of the whale population.

[0162] This embodiment uses a Chebyshev chaotic sequence with good autocorrelation and balance to initialize the position of the whale population. The specific operations are as follows:

[0163] Assume that the chaotic sequence generated in the D-dimensional Euclidean search space is expressed as:

[0164]

[0165] The mathematical expression for generating the Chebyshev chaotic map is:

[0166] y i+1,d = cos(χ·cos -1 y id ),y id ∈[-1,1](16);

[0167] Where χ is the order of the Chebyshev map; when used, a set of pseudo-random numbers y is generated arbitrarily within a given range. 1d ={y 1d ,d=1,2,…,D}, according to formula (15), we can get the uniformly distributed whole set of random number sequence y N×D , which is the Chebyshev chaotic sequence;

[0168] Mapping the chaotic sequence obtained by formula (16) into the algorithm solution space, the initialized whale population is obtained as:

[0169]

[0170] Among them, X che For the entire whale population; X i che is the i-th whale individual; X id che is the dth dimension where the i-th whale individual is located; X d max represents the upper limit of the dth dimension where the i-th whale individual is located, Xd min Indicates the lower limit of the value of the d-th dimension where the ith whale individual is located.

[0171] S503: Nonlinear Convergence Factor Design:

[0172] The nonlinear time-varying update strategy is used to update the convergence factor a:

[0173]

[0174] In the formula, n and m are constants; t is the number of current iterations; t max is the maximum number of iterations.

[0175] In the early stage of iteration, in order to enhance the ability of the algorithm to jump out of local extreme values, the convergence factor a should take a larger value. The combination of the sine function and the exponential decay function in the formula makes a rapidly increase in the initial stage. The sine function term provides the characteristic of rapid change, while the exponential term ensures the continuity and smoothness of the change. In the later stage of iteration, in order to improve the final convergence accuracy of the algorithm, the convergence factor a should take a smaller value and the decreasing speed should be slowed down.

[0176] S504: Adaptive inertia weight position adjustment design:

[0177] For some whale individuals X with good fitness i che (t), there is a high probability that there is an individual X in the local area where it is located that can update the global optimal position best (t), that is, the solutions of these individuals are better than the current global optimal. In order to quickly find these individuals and update the global optimal position, the inertia weight w of whale individuals with lower fitness should be reduced. i (t), thereby enhancing its local optimization ability. On the contrary, for individuals X with poor fitness i che (t), the quality of its current position is poor, and the probability that there is a better solution than the current global optimal solution in the region is low. In order to jump out of the current region and explore a better solution, the inertia weights w of these individuals should be increased. i (t), thereby enhancing its global optimization ability.

[0178] Set whale individual X i che The fitness of (t) is f i (t), the average fitness of the population is f min (t) is the optimal fitness of the current population. The population is divided into two subgroups based on the numerical relationship of fitness, and different inertia weights are used to adjust the positions of individuals. The inertia weight is designed as follows:

[0179]

[0180] Among them, w i (t) represents the inertia weight of the i-th whale individual in the t-th iteration; w max Indicates the upper limit of the inertia weight, w min Indicates the lower limit of the inertia weight.

[0181] S505: Main loop: An improved whale optimization algorithm performs N iterations, each iteration includes the following steps, see Figure 2 :

[0182] a. Perform population chaos initialization according to the method of S502;

[0183] b. Call the simulation module (Carsim-simulink);

[0184] c. Record and calculate the optimal individual fitness value f in the current population min (t) and the best position X best (t);

[0185] Among them, the optimal fitness represents the minimum fitness value, expressed as f min (t) indicates;

[0186] d. Update the nonlinear convergence factor a according to formula (18);

[0187] e. Update parameters A, C, l;

[0188] A=2ar1-a;C=2r2;where r1 and r2 are random vectors in [0,1]; a is the convergence factor, which gradually decreases to 0 as the number of iterations increases, expressed as: Where, t max is the maximum number of iterations. A and C are coefficient vectors in the whale optimization algorithm; l is a random number between [-1,1].

[0189] f. Calculate the average fitness value of the population Calculate the corresponding inertia weight w i (t);

[0190] The average fitness of the population is

[0191] g. Update the individual position according to the probability value p and the position parameter A (coefficient variable);

[0192] By introducing the inertia weight formula, we can get the individual position update formula:

[0193]

[0194] Among them, X i (t+1) is the position vector of the ith individual corresponding to the t+1th iteration, X best (t) is the best position vector among all individuals corresponding to the current iteration number; D is the distance vector between the best position and the individual position; X rand (t) represents the randomly selected whale position in the tth iteration; b is the logarithmic spiral shape constant; l is a random number between [-1,1];

[0195] S506: After N iterations, output the optimal rear wheel steering angle sliding mode controller parameters K1, ε1 and additional yaw moment sliding mode controller parameters K2, ε2.

[0196] The parameters that need to be determined in sliding mode control are K1, ε1, K2, ε2. These parameter variables are used as the position components of the whale, and the value range is [0.01,500]. The present invention designs an improved whale optimization algorithm (IWOA). The design of the fitness function is very important. The design of the fitness function in the algorithm is very important. The fitness function characterizes the adaptability of the population individuals to the current environment and the quality of the individuals. In order to ensure the vehicle path tracking accuracy, driving stability and better speed maintenance effect, a fitness function such as formula (14) is designed. According to Figure 2 The algorithm iteration process is to obtain the optimal whale position that minimizes the fitness function J through iteration, and then obtain the optimal simulation parameters K1, ε1, K2, ε2.

[0197] Step S6: According to Figure 3 The Ackerman steering principle and the dynamic loads on the front and rear axles of the vehicle redistribute the vehicle's turning angle and torque respectively. The step S6 specifically includes:

[0198] S601: The turning angle distributor receives the front wheel turning angle δ of the single-point preview controller f and the rear wheel steering angle δ of the sliding mode controller r , and redistributed according to the following Ackerman rotation relationship:

[0199]

[0200] In the formula, δ fl is the left front wheel turning angle; δ fr is the right front wheel turning angle; δ rl is the left rear wheel turning angle; δ rr is the right rear wheel turning angle; B f is the distance between the front wheels; Br is the distance between the rear wheels;

[0201] S602: The steering angle distributor receives the additional yaw moment ΔM and the longitudinal force F t , and redistribute according to the following estimated dynamic load relationship between the front and rear axles:

[0202]

[0203] Where, T fl is the left front wheel torque; T fr is the right front wheel torque; T rl is the left rear wheel torque; T rr is the torque of the right rear wheel; R is the wheel radius; F z_fl is the vertical load on the left front wheel; F z_fr is the vertical load on the right front wheel; F z_rl is the vertical load on the left rear wheel; F z_rr is the vertical load on the right rear wheel.

[0204] Figure 3 Schematic diagram of Ackerman steering principle, where O is the instantaneous center of vehicle steering.

[0205] Figure 4 The following are the comparison results of low-adhesion road conditions under different control strategies, which show the comparison of vehicle yaw rate and center of mass sideslip angle. Low-adhesion road conditions: The road adhesion coefficient is set to 0.4 and the double-lane route with a speed of 54km / h to simulate the scene of vehicle changing lanes and overtaking on low-speed low-adhesion roads. Among them, Controller 1: Carsim driver model control; Controller 2: Preview control, only controlling the front wheel steering for path tracking; Controller 3: 4WIS-4WID control; Controller 4 is the method provided by the present invention (i.e., IWOA-4WIS-4WID). As can be seen from the figure, in the absence of stability control, the yaw rate and center of mass slip angle of the vehicles controlled by controllers 1 and 2 have large changes. The maximum yaw rate of the IWOA-optimized 4WIS-4WID controller proposed by the present invention is 13.63° / s, which is reduced by about 19.49%, 16.99% and 4.62% compared with controllers 1, 2 and 3, respectively. The maximum center of mass slip angle is 0.0235°, which is much lower than other control strategies. The wheels can maintain good stability under this control strategy. That is, the control method provided by the present invention can ensure that the maximum yaw rate of the vehicle does not exceed 13.63° / s, and the maximum center of mass slip angle does not exceed 0.0235°.

[0206] Figure 5The results of the high-adhesion road condition comparison under different control strategies are shown, which shows the comparison of the vehicle's yaw rate and the sideslip angle of the center of mass. High-adhesion road condition: set to a double-lane change route with a road adhesion coefficient of 0.85 and a vehicle speed of 120km / h to simulate the scene of a vehicle changing lanes and overtaking at high speed on a good road. Among them, Controller 1: Carsim driver model control; Controller 2: Preview control, only controlling the front wheel steering for path tracking; Controller 3: 4WIS-4WID control; Controller 4 is the method provided by the present invention (i.e., IWOA-4WIS-4WID). As can be seen from the figure, in the absence of stability control, the yaw rate and center of mass slip angle of the vehicles controlled by controllers 1 and 2 have large changes. The maximum yaw rate of the 4WIS-4WID controller optimized by IWOA proposed in this paper is 11.68° / s, which is reduced by about 38.07%, 28.56% and 6.78% compared with controllers 1, 2 and 3, respectively. The maximum center of mass slip angle is 0.04119°, which is much lower than other control strategies. The wheels can maintain good stability under this control strategy. That is, the control method proposed in this invention can ensure that the maximum yaw rate of the vehicle does not exceed 11.68° / s, and the maximum center of mass slip angle does not exceed 0.04119°.

[0207] Summarize:

[0208] (1) Design a 4WIS+DYC coordinated control method for intelligent vehicles with four-wheel independent drive / independent steering systems. Compared with AFS and 4WIS vehicles, the 4WIS intelligent electric vehicle based on IWOA optimization can significantly improve the path tracking performance and handling stability of the vehicle by reasonably adjusting the steering angle and driving torque of each wheel, thereby greatly improving active safety.

[0209] (2) On low-speed and low-adhesion roads, the path tracking accuracy of vehicles using IWOA-4WIS-4WID is significantly improved, and they have better driving stability. Under high-speed and large-curvature driving conditions, vehicles using IWOA-4WIS-4WID still have better driving stability, but the control complexity brought by its high degree of freedom and the inconsistency of the dynamic response of each wheel lead to a lower path tracking accuracy compared to AFS vehicles, which still needs to be solved.

[0210] In addition to the above embodiments, the present invention may also have other implementation modes. Any changes, modifications, substitutions, combinations, and simplifications that deviate from the spirit and principle of the present invention shall be equivalent replacement modes and are included in the protection scope required by the present invention.

Claims

1. A distributed drive vehicle driving stability control method, characterized in that: The following steps are involved: Step S1: Based on the constant yaw rate assumption and in combination with the mathematical relationship of vehicle steering, a single-point preview deviation model is established to calculate the front wheel steering angle of the vehicle; Step S2: Design a rear wheel steering angle sliding mode controller to eliminate the deviation between the actual vehicle center of mass sideslip angle and the ideal center of mass sideslip angle; Step S3: designing an additional yaw moment sliding mode controller to eliminate the deviation between the actual vehicle yaw rate and the ideal yaw rate; Step S4: designing a longitudinal vehicle speed PID controller according to the deviation between the actual vehicle speed and the ideal vehicle speed; Step S5: determining an improved whale optimization algorithm by initializing the chaotic map, nonlinearly updating the convergence factor, and adjusting the position of the adaptive inertia weight, and performing parameter self-tuning on the sliding mode controllers of step S2 and step S3 according to the requirements of the fitness function; Step S6: Redistribute the vehicle steering angle and torque respectively according to the Ackerman steering principle and the dynamic loads of the front and rear axles of the vehicle.

2. A distributed drive vehicle driving stability control method according to claim 1, characterized in that: In step S1, based on the constant yaw rate assumption and combined with the mathematical relationship of vehicle steering, a single-point preview deviation model is established, specifically: Assume that the vehicle maintains a constant yaw rate for a period of time in the future, and the lateral speed v of the vehicle y Much smaller than the longitudinal speed v x , determine the combined speed of the vehicle The value remains unchanged; Based on this constant yaw rate assumption, the ideal yaw rate can be obtained: Where, ΔY is the lateral deviation between the preview point and the vehicle, t p Seconds is the preview time, β is the vehicle's center of mass side slip angle; Combined with the mathematical relationship of vehicle steering, the front wheel turning angle of the vehicle can be obtained as: Among them, h is the wheelbase of the front and rear axles of the vehicle, and K is the stability factor.

3. A distributed drive vehicle driving stability control method according to claim 1, characterized in that: The step S2 specifically includes: S201: Side slip angle deviation e β As the sliding mode control variable e1, the rear wheel angle δ r As the output variable u1, that is, e1 = e β =β-β d , u1=δ r , the integral sliding surface is designed as: Where β is the actual side slip angle of the vehicle's center of mass; β d is the ideal center of mass sideslip angle; are the proportional and integral coefficients respectively, both are non-zero real numbers; S202: Based on the traditional exponential convergence law Based on the continuous curve, the hysteresis of sgn(s) is overcome and the continuous curve is designed as follows: Among them, θ is a non-zero real number; The approach rate is designed to be Among them, s1 is the sliding surface; ε1 and K1 are non-zero coefficients; S203: Combine the two-degree-of-freedom vehicle model, and solve the equation e1=e β =β-β d Taking the derivative of both sides, we get: Where, m is the vehicle mass; l f is the distance from the front axle to the center of mass, l r is the distance from the rear axle to the center of mass; C f is the front wheel cornering stiffness; C r is the rear wheel cornering stiffness; γ is the actual yaw rate; Substituting the approach rate into the sliding surface, we get: Combining equations (5) and (6) we can get the rear wheel turning angle formula: Among them, δ r is the rear wheel turning angle.

4. A distributed drive vehicle driving stability control method according to claim 1, characterized in that: The step S3 specifically includes: S301: Compare the actual yaw rate γ with the ideal yaw rate γ d The deviation e γ As the sliding mode control variable e2, the additional yaw moment ΔM is used as the output variable u2, that is, e2 = e γ =γ-γ d , u2=ΔM, the integral sliding surface is designed as are the proportional coefficient and the integral coefficient respectively, both are non-zero real numbers; S302: The approach rate is designed to be Among them, s2 is the sliding surface; ε2 and K2 are non-zero coefficients; S303: Combine the two-degree-of-freedom vehicle model, and calculate the equation e2=e γ =γ-γ d Taking the derivative of both sides, we get: Among them, I z is the moment of inertia of the vehicle's center of mass about the axis perpendicular to the ground; Substituting the approach rate into the sliding surface, we get: Combining equations (9) and (10), we can get the additional yaw moment formula:

5. The method for controlling driving stability of a distributed drive vehicle according to claim 1, characterized in that: The step S4 specifically includes: S401, calculate speed deviation e v : yes v =v x_t -v x (12); In the formula, v x_t is the target vehicle speed, v x is the actual vehicle speed; S402, calculating the longitudinal moment of the vehicle: In the formula, k p , k i , k d They represent proportional, integral, and differential constants respectively.

6. A distributed drive vehicle driving stability control method according to claim 1, characterized in that: The step S5 specifically includes: S501: Input, output and fitness function: An improved whale optimization algorithm input includes: lateral deviation index J1, speed deviation index J2, yaw rate index J3 and center of mass sideslip angle index J4, population size and number of iterations; output includes rear wheel angle sliding mode controller parameters ε1, K1 and additional yaw moment sliding mode controller parameters ε2, K2; fitness function: Where c1, c2, c3 and c4 are non-zero weight coefficients, e d (t′) is the lateral deviation at time t′ within a T period, e v (t′) represents the velocity deviation at time t′ within a T cycle, γ(t′) is the yaw rate at time t′ within a T cycle, and β(t′) is the sideslip angle of the center of mass at time t′ within a T cycle; S502: Population chaos initialization design: Use Chebyshev chaotic sequence to initialize the position of the whale population, specifically: Assume that the chaotic sequence generated in the D-dimensional Euclidean search space is expressed as: The mathematical expression for generating the Chebyshev chaotic map is: and i+1,d =cos(χ·cos -1 and id ),and id ∈[-1,1](16); Where χ is the order of the Chebyshev map; when used, a set of pseudo-random numbers y is generated arbitrarily within a given range. 1d ={y 1d ,d=1,2,…,D}, according to formula (15), we can get the uniformly distributed whole set of random number sequence y N×D , which is the Chebyshev chaotic sequence; Mapping the chaotic sequence obtained by formula (16) into the algorithm solution space, the initialized whale population is obtained as: Among them, X che For the entire whale population; X i che is the i-th whale individual; X id che is the dth dimension where the i-th whale individual is located; X d max represents the i-th whale individual X i che The upper limit of the value of the dth dimension, X d min represents the i-th whale individual X i che The lower limit of the value of the dth dimension; S503: Nonlinear Convergence Factor Design: The nonlinear time-varying update strategy is used to update the convergence factor a: In the formula, n and m are constants; t is the number of iterations; t max is the maximum number of iterations; S504: Adaptive inertia weight position adjustment design: Set whale individual X i che The fitness of (t) is f i (t), the average fitness of the population is f min (t) is the optimal fitness of the current population. The population is divided into two subgroups based on the numerical relationship of fitness, and different inertia weights are used to adjust the positions of individuals. The inertia weight is designed as follows: Among them, w i (t) represents the inertia weight of the i-th whale individual in the t-th iteration; w max Indicates the upper limit of the inertia weight, w min Indicates the lower limit of the inertia weight; S505: Main loop: An improved whale optimization algorithm performs N iterations, each iteration includes the following steps: a. Population chaos initialization; b. Call the simulation module; c. Record and calculate the optimal individual fitness value and position in the current population; Among them, the optimal fitness represents the minimum fitness value, expressed as f min (t) indicates; d. Update the nonlinear convergence factor; e. Update parameters A, C, l; Among them, A and C are coefficient vectors in the whale optimization algorithm; l is a random number between [-1,1]; f. Calculate the average fitness value of the population and the corresponding inertia weight; The average fitness of the population is g. Update the individual position according to the probability value p and the position parameter A; S506: After N iterations, output the optimal rear wheel steering angle sliding mode controller parameters K1, ε1, additional yaw moment sliding mode controller parameters K2, ε2.

7. The method for controlling driving stability of a distributed drive vehicle according to claim 1, characterized in that: The step S6 specifically includes: S601: The turning angle distributor receives the front wheel turning angle δ of the single-point preview controller f and the rear wheel steering angle δ of the sliding mode controller r , and redistributed according to the following Ackerman rotation relationship: In the formula, δ fl is the left front wheel turning angle; δ fr is the right front wheel turning angle; δ rl is the left rear wheel turning angle; δ rr is the right rear wheel turning angle; B f is the distance between the front wheels; B r is the distance between the rear wheels; S602: The steering angle distributor receives the additional yaw moment ΔM and the longitudinal force F t , and redistribute according to the following estimated dynamic load relationship between the front and rear axles: Where, T fl is the left front wheel torque; T fr is the right front wheel torque; T rl is the left rear wheel torque; T rr is the torque of the right rear wheel; R is the wheel radius; F z_fl is the vertical load on the left front wheel; F z_fr is the vertical load on the right front wheel; F z_rl is the vertical load on the left rear wheel; F z_rr is the vertical load on the right rear wheel.

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