Distributed driving vehicle trajectory tracking control method considering energy consumption factor

Abstract

The invention belongs to the technical field of intelligent driving, and discloses a distributed driving vehicle trajectory tracking control method considering an energy consumption factor, and the method comprises the steps: obtaining a reference trajectory needing to be tracked, and constructing a trajectory tracking control model according to the reference trajectory; obtaining a front wheel rotation angle calculation formula and a rear wheel rotation angle calculation formula according to the trajectory tracking control model; delta f is equal to Kp1. E1 + Kd1. E2; delta r is equal to Kp2e1 + Kd2e2 + 1.5 * u2b; delta f is a front wheel turning angle, delta r is a rear wheel turning angle, Kp1 is a front wheel displacement error coefficient, Kd1 is a front wheel speed error coefficient, Kp2 is a rear wheel displacement error coefficient, Kd2 is a rear wheel speed error speed, e1 is a transverse displacement error, e2 is a transverse speed error, and u2b is a feedback quantity of the rear wheel turning angle; establishing a target function, and optimizing an array formed by the front wheel displacement error coefficient, the front wheel speed error coefficient, the rear wheel displacement error coefficient and the rear wheel speed error speed by taking the minimum target function as an optimization target to obtain an optimal array; and calculating according to the optimal array to obtain an optimal front wheel steering angle and an optimal rear wheel steering angle.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent driving technology, and in particular relates to a distributed drive vehicle trajectory tracking control method taking energy consumption factors into consideration. Background Art

[0002] Trajectory tracking is an important challenge for the rapid development of the intelligent driving industry. As a core component of intelligent driving technology, the vehicle trajectory tracking control strategy is not only related to driving accuracy and comfort, but also directly affects the energy efficiency of the vehicle. Therefore, studying the vehicle trajectory tracking control strategy considering energy consumption is of great significance for improving the energy-saving performance of automobiles and promoting sustainable transportation development.

[0003] Most of the existing trajectory tracking control technologies take stability as the control objective and cannot take energy consumption factors into consideration. Summary of the invention

[0004] The purpose of the present invention is to provide a distributed drive vehicle trajectory tracking control method taking energy consumption factors into consideration, which integrates energy-saving design while performing tracking control, and can reduce energy consumption while maintaining high trajectory tracking accuracy.

[0005] The technical solution provided by the present invention is:

[0006] A distributed drive vehicle trajectory tracking control method considering energy consumption factors comprises the following steps:

[0007] Step 1: Obtain a reference trajectory to be tracked, and construct a trajectory tracking control model based on the reference trajectory;

[0008] Step 2: obtaining a front wheel angle calculation formula and a rear wheel angle calculation formula according to the trajectory tracking control model;

[0009] δ f =K p1 e1+K d1 ·e2;

[0010] δ r =K p2 e1+K d2 ·e2+1.5·u 2b ;

[0011] Among them, δ f is the front wheel turning angle, δ r is the rear wheel turning angle, K p1 is the front wheel displacement error coefficient, K d1 is the front wheel speed error coefficient, K p2 is the rear wheel displacement error coefficient, K d2is the rear wheel speed error speed, e1 is the lateral displacement error, e2 is the lateral speed error, u 2b is the feedback amount of the rear wheel steering angle;

[0012] Step 3, establishing an objective function, and taking the minimum of the objective function as the optimization goal, optimizing the array consisting of the front wheel displacement error coefficient, the front wheel speed error coefficient, the rear wheel displacement error coefficient and the rear wheel speed error speed to obtain the optimal array;

[0013] Step 4: Calculate the optimal front wheel steering angle and the optimal rear wheel steering angle according to the optimal array.

[0014] Preferably, the trajectory tracking control model is:

[0015]

[0016] Among them, l f is the distance from the center of mass to the front axle, l r is the distance from the center of mass to the rear axle, C pf is the front wheel cornering stiffness under the single-track model, C pr is the rear wheel cornering stiffness under the single-track model, C lf is the longitudinal stiffness of the front wheel in the single-track model, C lr is the longitudinal stiffness of the rear wheel under the single-track model, s f is the front wheel slip rate under the single track model, s r is the rear wheel slip rate under the single-track model, m is the vehicle mass, I z is the moment of inertia of the vehicle around the z-axis, is the longitudinal velocity in the global coordinate system, Transverse velocity in global coordinate system, v y is the lateral velocity, v x is the longitudinal velocity, is the yaw angle, δ f is the front wheel turning angle, δ r is the rear wheel turning angle, is the longitudinal acceleration, is the yaw angular velocity, is the lateral acceleration, is the yaw angular acceleration.

[0017] Preferably, the feedback amount of the rear wheel steering angle is calculated by the following formula:

[0018]

[0019] in, is the derivative of the speed error with feedback link, is the derivative of the yaw rate error with feedback link; c1 and c2 are constants, g = 9.8 m / s2 is the acceleration due to gravity.

[0020] Preferably, the objective function is:

[0021]

[0022] Among them, e1 is the lateral displacement error, e2 is the lateral velocity error, e3 is the yaw angle error, e4 is the yaw angular velocity error, q1, q2, q3, q4 are the weight coefficients of each error, q e is the economic weight coefficient, τ is the time, T ∞ is the final state, n is the motor speed, T is the motor torque, η T is the motor efficiency.

[0023] Preferably, the relationship between the lateral displacement error, the lateral velocity error, the yaw angle error and the yaw angular velocity error and the front wheel displacement error coefficient, the front wheel velocity error coefficient, the rear wheel displacement error coefficient and the rear wheel velocity error coefficient is:

[0024]

[0025] Among them, y r is the reference lateral displacement, is the reference lateral velocity, is the reference lateral acceleration, is the reference yaw angle, is the reference yaw rate, is the reference yaw angular acceleration, A is the first error coefficient matrix, B is the second error coefficient matrix, are the derivatives of e1, e2, e3, and e4 respectively.

[0026] Preferably, in step 3, the front wheel displacement error coefficient K is optimized using the Black Hawk optimization algorithm. p1 , the front wheel speed error coefficient K d1 , the rear wheel displacement error coefficient K p2 and the rear wheel speed error speed K d2 The optimization of the array consists of the following steps:

[0027] Step 1: Take the parameter array to be optimized {K p1 K d1 K p2 K d2} as an individual in the population to be optimized, and initialize the population to be optimized to obtain the initialized population;

[0028] Among them, the number of individuals in the initial population is n;

[0029] Step 2: Using the objective function as the fitness function; using the Black Hawk optimization algorithm to update the individuals in the population, and outputting n individuals with smaller fitness as the optimal solution set after each round of update, as the next generation population;

[0030] Step 3: Repeat step 2 to iteratively update the individuals in the population until the set number of iterations is reached;

[0031] Step 4: Select the individual with the smallest fitness from the optimal solution set obtained in the last iteration as the optimization result.

[0032] Preferably, in step 4, if the optimal solution set obtained in the last iteration has multiple individuals with the same fitness, and all of them are equal to the minimum fitness, then the economic terms J in the objective function corresponding to these individuals are calculated. e , and choose J e The smallest individual is taken as the optimization result;

[0033] in:

[0034] The beneficial effects of the present invention are:

[0035] The distributed drive vehicle trajectory tracking control method taking energy consumption factors into consideration provided by the present invention integrates energy-saving design while performing tracking control by optimizing algorithms and system architecture, and can reduce energy consumption while maintaining high trajectory tracking accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a schematic diagram of the trajectory tracking control model described in the present invention.

[0037] Figure 2 The present invention provides a flow chart for optimizing an array consisting of a front wheel displacement error coefficient, a front wheel speed error coefficient, a rear wheel displacement error coefficient and a rear wheel speed error speed.

[0038] Figure 3 The figure is a flow chart of the Black Hawk optimization algorithm described in the present invention. DETAILED DESCRIPTION

[0039] The present invention is further described in detail below in conjunction with the accompanying drawings so that those skilled in the art can implement the invention with reference to the description.

[0040] like Figure 1-3 As shown, the present invention provides a distributed drive vehicle trajectory tracking control method considering energy consumption factors, and the specific implementation process is as follows.

[0041] The current parameters of the vehicle are calculated based on the working conditions and the target reference trajectory (the reference trajectory to be tracked), using the following formula:

[0042]

[0043] in, represents the longitudinal velocity in the global coordinate system, represents the lateral velocity in the global coordinate system, y represents the lateral displacement, and v y represents the lateral velocity, v x Indicates the longitudinal speed, represents the yaw angle, and τ represents the time.

[0044] like Figure 1 As shown, the trajectory tracking coordinate model is calculated based on the vehicle specific parameters and current parameters:

[0045]

[0046] Among them, l f is the distance from the center of mass to the front axle, l r is the distance from the center of mass to the rear axle, C pf is the front wheel cornering stiffness under the single-track model, C pr is the rear wheel cornering stiffness under the single-track model, C lf is the longitudinal stiffness of the front wheel under the single-track model, C lr is the longitudinal stiffness of the rear wheel under the single-track model, s f is the front wheel slip rate under the single track model, s r is the rear wheel slip rate under the single-track model, m is the vehicle mass, I z is the moment of inertia of the vehicle around the z-axis; the above are the vehicle's own parameters.

[0047] Front wheel turning angle δ f , rear wheel turning angle δ r , longitudinal acceleration Yaw rate Lateral acceleration and the yaw angular acceleration It is the current parameter of the vehicle when it is running.

[0048] Calculate δ based on the trajectory tracking coordinate model f ,δ r :

[0049]

[0050] e rr =[e1 e2 e3 e4] T

[0051] Where y is the lateral displacement, is the lateral velocity, is the yaw angle, is the yaw angular velocity, y r is the reference lateral displacement, is the reference lateral velocity, is the reference yaw angle, is the reference yaw rate, e1 is the lateral displacement error, e2 is the lateral velocity error, e3 is the yaw angle error, e4 is the yaw rate error, and e rr is the error matrix composed of e1, e2, e3, and e4.

[0052]

[0053] Among them, s1 is the speed error with feedback link, s2 is the yaw angular velocity error with feedback link; c1 and c2 are constants.

[0054]

[0055] Among them, λ1, λ2, λ3, and λ4 are the approach rate functions respectively; ψ and ξ are constants

[0056] The derivative of the error with feedback is calculated using the formula:

[0057]

[0058] Among them, κ1, κ2, κ3, and κ4 are feedback parameters respectively; is the derivative of the speed error with feedback link, is the derivative of the yaw rate error with feedback.

[0059] Derivation of intermediate variable u according to trajectory tracking model formula 1f 、u 2f ,f,g:

[0060]

[0061] Use the formula to calculate the feedback amount u of the rear wheel steering angle 2b :

[0062]

[0063] Get the front and rear wheel turning angle formula:

[0064] δ f =K p1 e1+K d1 e2

[0065] δ r =K p2 e1+K d2 ·e2+1.5·u 2b

[0066] Among them, K p1 is the front wheel displacement error coefficient, K d1 is the front wheel speed error coefficient, K p2 is the rear wheel displacement error coefficient, K d2 is the rear wheel speed error speed

[0067] According to the trajectory tracking coordinate model simplification, we can get:

[0068]

[0069] The above state space equation can be expressed as follows:

[0070]

[0071] That is, we get:

[0072]

[0073] Taking the trajectory tracking accuracy as the optimization goal, K p1 , K d1 , K p2 , K d2 Iterative optimization is performed, and its performance index is expressed as:

[0074]

[0075] Among them, A is the first error coefficient matrix, B is the second error coefficient matrix, q1, q2, q3, q4 are the weight coefficients of each error quantity. Since most working conditions are in high-speed conditions, the lateral speed and yaw rate have high requirements, so the lateral speed and yaw rate are set to higher order quantities to ensure stability under high-speed conditions. e is the economic weight coefficient, τ is the time, T ∞ is the final state moment, n is the motor speed, obtained by the sensor, T is the motor torque, obtained by the sensor, η T is the motor efficiency, obtained from the motor map.

[0076] In order to achieve the optimization parameter K p1 , K d1 , K p2 , K d2 To adjust the control quantity U = [δ f δ r ] T , thereby changing the state quantity Then the adjustment error matrix e is achieved rr =[e1 e2 e3 e4] T purpose.

[0077] In this embodiment, for the performance indicator J, the BEO algorithm is used to calculate K p1 , K d1 , K p2 , K d2 Perform iterative optimization to find the optimal solution in a large range of iteration space. The specific process is as follows.

[0078] The BEO algorithm is inspired by the behavior of black hawks, whose main biological behaviors include predation, migration, and reproduction. Predation behavior can be subdivided into tracking, circling, capturing, snatching, and warning; reproductive behavior includes courtship and hatching.

[0079] The mathematical model of the BEO algorithm will be constructed from the aspects of the black hawk's tracking, circling, capturing, snatching, warning, migration, courtship and hatching behaviors.

[0080] initialization

[0081] First, the number of individuals in the population is set to n, the dimension of the variable to be optimized is d = 4, and K is used to represent the black hawk population matrix. i=1,2,...,n represents different black hawk individuals to be optimized:

[0082] K j =lb+rand·(ub-lb),j=1,2,...,n;

[0083] Among them, lb and ub are the upper and lower bounds of the optimization variable respectively; rand is a column vector with a dimension of 4, whose elements are random values ​​from 0 to 1. The fitness value of the individual is expressed by f K In this case, f K =J.

[0084] After initialization, the "tracking" step is performed.

[0085] The tracking process uses the following formula to update the individual:

[0086]

[0087] Among them, K r is a random position in the search space, K k is the position of a random individual, K best is the current best solution set; D is for K best The farthest distance from the search boundary, t represents the number of iterations; r1 is a random number between 0 and 1, and t1 is a random number between 0 and 1 generated by tent_map. is the auxiliary tracking population matrix of the t+1th time, K t+1 is the tracking population matrix of the t+1th time, α is the tracking update auxiliary function, is the t-th random individual position, is the optimal solution for the tth iteration.

[0088] Use the formula to track and calculate candidate solutions and their corresponding fitness values ​​f st , and temporarily record it as the minimum fitness value f best .

[0089] Among them, tent_map is as follows:

[0090]

[0091] Among them, x and a are two independent variables of tent_map.

[0092] Judge K best Whether the search boundary is reached. If so, execute the "Warning" step to generate a new candidate solution to replace the solution beyond the boundary, and then continue to execute the "Circling", "Capturing", and "Snatching" steps to update; if not, directly execute the "Circling", "Capturing", and "Snatching" steps to update.

[0093] The warning process update formula is:

[0094]

[0095] in, ζ=-10d sni +15, d si is the i-th dimension The distance from the center of the search space, d smin and d smax are the minimum and maximum d si , It's K t Follow the approach The position matrix after rearrangement of the order. ζ is the boundary auxiliary function, θ n is the sine auxiliary function, Update the auxiliary matrix for the t+1th warning, (i, j) represents the i-th row and j-th column, and P(j) is the probability density function of the Poisson distribution.

[0096] Use the warning process update formula to calculate the candidate solution and fitness value f w , and replace f with the smaller fitness value best And record the candidate solution set.

[0097] Warning: The update formula uses Poisson distribution to guide the movement of particles and generate new K t+1 Used to replace solutions that exceed the search boundary.

[0098] The circling process uses the following formula to update individuals:

[0099]

[0100] in, M is a 4-dimensional spiral matrix, a=r2·2π, a is the spiral angle; r2 is a random number between 0 and 1.

[0101] The candidate solution and fitness value f are calculated by the spiral process update formula ho , and replace f with the smaller fitness value best And record the candidate solution set.

[0102] The function of the hovering operation is to perform a rotation search to further improve the range of the global optimal position. The hovering strategy performs a rotation search within the range of the global optimal position initially determined by the tracking strategy.

[0103] The capture process uses the following formula to update individuals:

[0104]

[0105] in, D1 is the first position adjustment factor, D2 is the second position adjustment factor, s0 is a column vector with a dimension of 4, and its elements are between 0.5 and 1; K * Represents the capture update auxiliary matrix.

[0106] Similarly, the capture process update formula is used to calculate the candidate solution and fitness value f c , and replace f with the smaller fitness value best And record the candidate solution set.

[0107] The snatching process uses the following formula to update individuals:

[0108]

[0109] Among them, r3 is a random vector with dimension d, and the values ​​of elements in each dimension obey the normal distribution.

[0110] Use the snatch process update formula to calculate the candidate solution and fitness value f sn , and replace f with the smaller fitness value best And record the candidate solution set.

[0111] The snatching process update formula simplifies the snatching behavior into a black hawk flying from one point to another in a curve. Then the jumping motion of the point is used to simulate the snatching behavior of the black hawk. The snatching strategy is a jump search method, which aims to perform a jump search on the position adjusted by the previous strategy to increase the probability of finding the global optimal position.

[0112] Determine the current number of iterations t and the maximum number of iterations Does the relationship between the number of times H the update is stopped (the number of iterations eliminated during the iteration process) satisfy: If satisfied: execute the "migration" step to update; if not satisfied: execute the "courtship" and "hatching" steps to update and end the cycle.

[0113] The migration process uses the following formula to update individuals:

[0114]

[0115] in, f best is the current best fit value, f(j) is the fit value of the jth individual, s1 is the migration update auxiliary function, which is a column vector with a dimension of 4, and the elements are between -1 and 1, t2 is a random number from 0.4 to 1 formed by tent mapping, and ε is a constant. z is the auxiliary migration function

[0116] Use the migration process to update the formula to calculate the candidate solution and fitness value f im , and replace f with the smaller fitness value best And record the candidate solution set.

[0117] The migration process update formula uses the fitness function value to represent the black hawk's adaptability to the environment, and constructs a migration function z(f) to further simulate its migration law: the lower the fitness, the farther the migration distance. The migration mechanism aims to migrate individuals with lower fitness to places far away from the current optimal position to reduce the possibility of falling into the local optimum.

[0118] After performing the "Migration" step to update, determine whether the following conditions are met: If satisfied: execute the "courtship" and "hatching" steps to update and end the loop; if not satisfied: jump back to initialization and re-enter the loop.

[0119] The courtship process uses the following formula to update individuals:

[0120]

[0121] in, Q is the step factor, and its function expression comes from the transformation of the Sigmoid function; r4 and r6 are random numbers between 0 and 1, and r5 and r7 are column vectors with a dimension of 4, whose elements follow a normal distribution. j%2 means the remainder when j is divided by 2, for example: j%2=1 means the remainder when j is divided by 2 is 1, which means j is an odd number.

[0122] Use the courtship process update formula to calculate candidate solutions and fitness value f c , and replace f with the smaller fitness value best And record the candidate solution set.

[0123] The courtship process update formula utilizes similar fluctuations between sine and cosine functions and alternating patterns between odd and even numbers to simulate the interactive behaviors of male and female black hawks during courtship.

[0124] The incubation process uses the following formula to update individuals:

[0125]

[0126] Among them, R is a set of normally distributed arrays, K d is the group position matrix after rearranging the group position K from closest to the nest to farthest

[0127] Use the incubation process update formula to calculate candidate solutions and fitness value f ha , and replace f with the smaller fitness value best And record the candidate solution set.

[0128] The incubation update formula simulates the situation that male black eagles are more active near the nest and less active farther away during the incubation period due to guarding the incubation of female black eagles according to the normal distribution. The incubation strategy is a diffusion search method that allows some individuals to continue to approach the current optimal value while others spread outward, thus preventing them from falling into the local optimal value.

[0129] When the iterative optimization is completed, the BEO optimization algorithm obtains the optimal solution. If the optimization obtains more than one set of optimal solutions K, that is, multiple sets of optimal solutions are obtained, then the one that meets the performance index is selected. China Economic Item The smallest set of solutions is considered the only optimal solution.

[0130] The distributed drive vehicle trajectory tracking control method taking energy consumption factors into consideration provided by the present invention successfully integrates energy-saving design while maintaining high trajectory tracking accuracy. By optimizing the algorithm and system architecture, energy consumption is significantly reduced, which reflects the emphasis on efficient energy utilization and sustainable development, and is of great significance to promoting technological progress in related industries.

[0131] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and the implementation modes, and they can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and the illustrations shown and described herein.

Claims

1. A distributed drive vehicle trajectory tracking control method considering energy consumption factors, characterized in that: The steps include: Step 1: Obtain a reference trajectory to be tracked, and construct a trajectory tracking control model based on the reference trajectory; Step 2: Obtaining a front wheel angle calculation formula and a rear wheel angle calculation formula based on the trajectory tracking control model; δ f =K p1 ·e1+K d1 ·e2; δ r =K p2 e1+K d2 e2+1.5 u 2b ; Among them, δ f is the front wheel turning angle, δ r is the rear wheel turning angle, K p1 is the front wheel displacement error coefficient, K d1 is the front wheel speed error coefficient, K p2 is the rear wheel displacement error coefficient, K d2 is the rear wheel speed error speed, e1 is the lateral displacement error, e2 is the lateral speed error, u 2b is the feedback amount of the rear wheel angle; Step 3: Establish an objective function, and optimize the array consisting of the front wheel displacement error coefficient, the front wheel speed error coefficient, the rear wheel displacement error coefficient, and the rear wheel speed error speed with the minimum of the objective function as the optimization goal to obtain the optimal array; Step 4: Calculate the optimal front wheel steering angle and the optimal rear wheel steering angle according to the optimal array.

2. The distributed drive vehicle trajectory tracking control method considering energy consumption factors according to claim 1 is characterized in that: The trajectory tracking control model is: Among them, l f is the distance from the center of mass to the front axle, l r is the distance from the center of mass to the rear axle, C pf is the front wheel cornering stiffness under the single-track model, C pr is the rear wheel cornering stiffness under the single-track model, C lf is the longitudinal stiffness of the front wheel under the single-track model, C lr is the longitudinal stiffness of the rear wheel under the single-track model, s f is the front wheel slip rate under the single track model, s r is the rear wheel slip rate under the single-track model, m is the vehicle mass, I z is the vehicle's moment of inertia around the z-axis, is the longitudinal velocity in the global coordinate system, Transverse velocity in the global coordinate system, v y is the lateral velocity, v x is the longitudinal velocity, is the yaw angle, δ f is the front wheel turning angle, δ r is the rear wheel turning angle, is the longitudinal acceleration, is the yaw angular velocity, is the lateral acceleration, is the yaw angular acceleration.

3. The distributed drive vehicle trajectory tracking control method considering energy consumption factors according to claim 2, characterized in that: The feedback amount of the rear wheel steering angle is calculated using the following formula: in, is the derivative of the speed error with feedback link, is the derivative of the yaw rate error with feedback; c1 and c2 are constants, g = 9.8 m / s 2 is the acceleration due to gravity.

4. The distributed drive vehicle trajectory tracking control method considering energy consumption factors according to any one of claims 1 to 3, characterized in that: The objective function is: Among them, e1 is the lateral displacement error, e2 is the lateral velocity error, e3 is the yaw angle error, e4 is the yaw angular velocity error, q1, q2, q3, q4 are the weight coefficients of each error amount, q e is the economic weight coefficient, τ is time, T ∞ is the final state moment, n is the motor speed, T is the motor torque, η T is the motor efficiency.

5. The distributed drive vehicle trajectory tracking control method considering energy consumption factors according to claim 4 is characterized in that: The relationship between the lateral displacement error, lateral velocity error, yaw angle error and yaw angular velocity error and the front wheel displacement error coefficient, front wheel velocity error coefficient, rear wheel displacement error coefficient and rear wheel velocity error speed is: Among them, y r is the reference lateral displacement, is the reference lateral velocity, is the reference lateral acceleration, is the reference yaw angle, is the reference yaw rate, is the reference yaw angular acceleration, A is the first error coefficient matrix, B is the second error coefficient matrix, are the derivatives of e1, e2, e3, and e4 respectively.

6. The distributed drive vehicle trajectory tracking control method considering energy consumption factors according to claim 5 is characterized in that: In step 3, the Black Hawk optimization algorithm is used to calculate the front wheel displacement error coefficient K p1 , the front wheel speed error coefficient K d1 , the rear wheel displacement error coefficient K p2 and the rear wheel speed error speed K d2 The optimization of the array consists of the following steps: Step 1: The parameter array to be optimized {K p1 K d1 K p2 K d2 } as an individual in the population to be optimized, and initialize the population to be optimized to obtain the initialized population; Among them, the number of individuals in the initial population is n; Step 2: Using the objective function as the fitness function, the Black Hawk optimization algorithm is used to update the individuals in the population. After each round of update, n individuals with the smallest fitness are output as the optimal solution set, which is used as the next generation population. Step 3: Repeat step 2 and iteratively update the individuals in the population until the set number of iterations is reached; Step 4: Select the individual with the smallest fitness from the optimal solution set obtained in the last iteration as the optimization result.

7. The distributed drive vehicle trajectory tracking control method considering energy consumption factors according to claim 6, characterized in that: In step 4, if the optimal solution set obtained in the last iteration has multiple individuals with the same fitness, and all of them are equal to the minimum fitness, then calculate the economic terms J in the objective function corresponding to these individuals. e , and choose J e The smallest individual is taken as the optimization result; in:

Images