A trajectory tracking control method for distributed controlled-by-wire vehicles considering communication delay

Abstract

The present invention discloses a distributed wire-controlled vehicle trajectory tracking control method considering communication delay, including: step 1, establishing a distributed wire-controlled vehicle nonlinear two-degree-of-freedom dynamics model based on the magic formula; step 2, establishing a nonlinear system trajectory tracking controller with communication delay characteristics based on the nonlinear two-degree-of-freedom dynamics model; step 3, establishing an error function and a nonlinear system trajectory tracking controller evaluation index, and obtaining the optimal controller parameters based on the BEO algorithm. Based on the magic formula, the communication delay is considered in the model establishment, and the controller parameters are optimized through the BEO algorithm to improve the controller accuracy, ensure that the autonomous driving vehicle accurately follows the preset path, and improve road safety and traffic efficiency.

Description

Technical Field

[0001] The invention relates to a distributed wire-controlled vehicle trajectory tracking control method considering communication delay, and belongs to the field of distributed wire-controlled vehicle trajectory tracking control. Background Art

[0002] The research on trajectory tracking of electric vehicles has important practical significance and broad application prospects. On the one hand, trajectory tracking technology provides a prerequisite for realizing intelligent driving, and can enable distributed wire-controlled vehicles to achieve accurate navigation in complex traffic environments. On the other hand, trajectory tracking technology can effectively reduce traffic accidents and improve driving safety. By optimizing vehicle paths and reducing energy consumption, trajectory tracking can also promote energy conservation and environmental protection, and reduce carbon emissions. The research on trajectory tracking of electric vehicles has a far-reaching impact on the development of future transportation. Existing technologies usually use PID or MPC controllers to solve trajectory tracking problems. However, since distributed wire-controlled vehicles often have strong nonlinear systems and signal transmission delays, existing methods are often less adaptable when dealing with nonlinear systems and communication delays, and it is difficult to be widely used in practice; on the other hand, most of the algorithms currently used require a priori presets, which to a large extent cannot meet the actual control needs of distributed wire-controlled vehicles.

[0003] To this end, the present invention proposes a trajectory tracking control and optimization method for a distributed wire-controlled vehicle nonlinear system taking into account delayed communication, which can effectively improve the above-mentioned problems. Summary of the invention

[0004] The present invention designs and develops a distributed wire-controlled vehicle trajectory tracking control method that takes communication delay into consideration. Based on the magic formula, the communication delay is taken into consideration in the model establishment, and the controller parameters are optimized through the BEO algorithm, and the system performance is maximized, the controller accuracy is improved, and the autonomous driving vehicle is ensured to accurately follow the preset path, thereby improving road safety and traffic efficiency.

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

[0006] A trajectory tracking control and optimization method for a distributed wire-controlled vehicle nonlinear system considering communication delay, comprising:

[0007] Step 1: Establish a nonlinear two-degree-of-freedom dynamic model of a distributed controlled-by-wire vehicle based on the magic formula;

[0008] Step 2: Based on the nonlinear two-degree-of-freedom dynamics model, a nonlinear system trajectory tracking controller with communication delay characteristics is established;

[0009] Step 3: Establish the error function and evaluation index of the nonlinear system trajectory tracking controller, and obtain the optimal controller parameters based on the BEO algorithm.

[0010] Preferably, the magic formula is:

[0011] ;

[0012] In the formula, is the lateral force, is the slip angle, is the stiffness factor, is the curvature factor, is the shape factor, is the peak factor, , It is the intermediate variable in the magic formula;

[0013] Based on the magic formula and considering the communication delay, a nonlinear model of trajectory tracking is established: ;

[0014] In the formula, For no delay time, Delay time for the left front wheel, is the delay time of the left rear wheel and the delay time of the right rear wheel, is the yaw angular acceleration without delay, For left front wheel Directional force, is the delayed sideslip angle of the left front wheel, is the delayed sideslip angle of the left rear front wheel, is the delayed yaw rate of the left front wheel, is the delayed yaw velocity of the left rear wheel, is the undelayed yaw rate, is the distance from the center of mass to the front axle, For left rear wheel Directional force, For right rear wheel Directional force, is the distance from the center of mass to the rear axle, is the front wheel turning angle, is the vehicle mass, For vehicle speed Axis weight For vehicles around The moment of inertia of the shaft rotation.

[0015] Preferably,

[0016] set up is a constant 10 ,Pick .

[0017] Preferably, the step 2 comprises:

[0018] Constrain the lateral displacement to Set the helper function for:

[0019] ;

[0020] In the formula, is the estimated convergence time;

[0021] From the output constraints we can get:

[0022] ;

[0023] In the formula, is the maximum lateral displacement, is the minimum lateral displacement, is the reference lateral displacement, is the lateral error;

[0024] Use the inverse adjustment function:

[0025] ;

[0026] In the formula, is the maximum reverse regulation function, is the minimum inverse regulation function;

[0027] Use penalty functions to limit :

[0028] ;

[0029] In the formula, is the constant to be optimized, is the penalty function;

[0030] Set up the helper function:

[0031]

[0032] In the formula, is a positive constant to be optimized;

[0033] Setting function:

[0034] ;

[0035] and, Requirements: , its derivative Need to meet , is the auxiliary convergence exponential function, for The derivative of

[0036] Final control volume for:

[0037] ;

[0038] Determine the parameters to be optimized as , , .

[0039] Preferably, the step three comprises:

[0040] Establish error function and controller evaluation index: ,

[0041] Set the population size to , the dimension of the variable to be optimized is , , is the black hawk population matrix,

[0042] ;

[0043] ;

[0044] In the formula, are different black hawk individuals to be optimized, For each column of the population matrix, that is, different black hawk individuals, and are the lower and upper boundaries of the search space respectively; Is the dimension A column vector whose elements are random values ​​from 0 to 1;

[0045] Based on the BEO algorithm, iterative optimization is performed. The black hawk population matrix to be optimized undergoes the following steps: tracking, circling, capturing, snatching, warning, migrating, courting, and hatching. The optimal solution obtained in the last iteration is selected to meet the controller evaluation index. The smallest solution is taken as the optimal solution, and the optimal parameters are obtained. , , .

[0046] Beneficial effects of the present invention: In view of the inherent complex nonlinear characteristics of the vehicle system, the nonlinear delay controller introduced by this method, with its excellent adaptive ability and robust design, can effectively resist external variable interference and uncertainty, ensure that the vehicle can maintain stable control under various complex driving conditions, and significantly improve driving safety and system reliability. The controller provided by this method can not only accurately model and compensate for the time delay in the system, realize high-precision driving command execution, ensure that the vehicle trajectory accurately follows the predetermined route, and show excellent path tracking capabilities, but also its real-time state monitoring and dynamic adjustment mechanism enable the controller to quickly respond to changes in vehicle status, flexibly adjust the control strategy, effectively overcome performance fluctuations caused by delays or environmental changes, and ensure the smoothness and efficiency of the driving process. In addition, the high flexibility of the controller design allows customized optimization according to different vehicle models, driving scenarios and performance requirements, maximizes the potential of the vehicle, and improves the overall driving experience and vehicle performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a framework diagram of the control method described in the present invention.

[0048] Figure 2 This is a trajectory tracking coordinate model diagram described in the present invention.

[0049] Figure 3 Flow chart of the BEO algorithm described in the present invention.

[0050] Figure 4 This is a comparison diagram of the lateral errors of the double-lane shifting described in the present invention.

[0051] Figure 5 This is a comparison diagram of the double lane shifting trajectories described in the present invention.

[0052] Figure 6 This is a comparison diagram of the double-lane-shifting yaw angles of the present invention. DETAILED DESCRIPTION

[0053] 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.

[0054] like Figure 1-5 As shown, the present invention provides a distributed wire-controlled vehicle trajectory tracking control method considering communication delay, based on the magic formula, considering communication delay in model establishment, and optimizing controller parameters and improving controller accuracy through the BEO algorithm, including:

[0055] Step 1: Establish a nonlinear two-degree-of-freedom dynamic model of a distributed controlled-by-wire vehicle based on the magic formula;

[0056] Step 2: Based on the nonlinear two-degree-of-freedom dynamics model, a nonlinear system trajectory tracking controller with communication delay characteristics is established;

[0057] Step 3: Establish the error function and evaluation index of the nonlinear system trajectory tracking controller, and obtain the optimal controller parameters based on the BEO algorithm.

[0058] The establishment of nonlinear two-degree-of-freedom dynamics model includes:

[0059] Taking the body coordinate system of the distributed wire-controlled vehicle as a reference, the origin is set as the center of mass of the vehicle At the location, based on the “right-hand rule”, the direction along which the vehicle moves is set to Positive axis direction, vertical The axis along the left side is set to Axis positive direction, and They are the vehicle speed in its own coordinate system. Axis component, in , is the center of mass sideslip angle, in , is the yaw angle, in units of , is the yaw angular velocity, in units of , and are the distances from the vehicle's center of mass to the front and rear axles, respectively, in units of In the present invention, as a preferred embodiment, , They are: left front wheel The force in the axial direction, the right front wheel The force in the axial direction, the left rear wheel The force in the axial direction, the right rear wheel The force in the axial direction is , They are: left front wheel The force in the axial direction, the right front wheel The force in the axial direction, the left rear wheel The force in the axial direction, the right rear wheel The force in the axial direction is ; are the left front wheel lateral force and the right front wheel lateral force, in units of ; They are the speed direction of the left front wheel, the speed direction of the right front wheel, the speed direction of the left rear wheel, and the speed direction of the right rear wheel. They are the left front wheel slip angle, right front wheel slip angle, left rear wheel slip angle, and right rear wheel slip angle, in units of , They are the left front wheel angle and the right front wheel angle respectively, in units of .

[0060] In the present invention, as a preferred embodiment, the longitudinal vehicle speed is simplified to a constant ,set up =10 , at this time the longitudinal acceleration is 0, longitudinal force All are 0

[0061] According to Newton's second law and force analysis, the dynamic equations of the vehicle in the two degrees of freedom of lateral motion and yaw motion can be obtained:

[0062] Lateral Movement:

[0063] ;

[0064] Yaw motion:

[0065] ;

[0066] in, is the vehicle curb weight, is the lateral acceleration component of the vehicle , For vehicles around Moment of inertia of shaft rotation , is the yaw angular acceleration;

[0067] In the present invention, as a preferred embodiment, 1528 , is 4178 .

[0068] Simplifying the lateral motion formula, we get the following formula:

[0069] ;

[0070] In the formula, is the sideslip angular velocity of the center of mass;

[0071] Side slip angle Calculated by the following formula:

[0072] ;

[0073] In the formula, They are the left front wheel slip angle, right front wheel slip angle, left rear wheel slip angle, and right rear wheel slip angle, in units of .

[0074] In order to better control the trajectory tracking accuracy, the tire model of the distributed wire-controlled vehicle uses the most commonly used "magic formula" tire model. Based on the kingpin zero offset characteristics of the distributed wire-controlled vehicle, the roll force on the tire is almost zero, so the influence of the roll force can be ignored, which relatively reduces the complexity.

[0075] Use the "magic formula" as follows:

[0076] ;

[0077] In the formula, is the stiffness factor, is the shape factor, is the peak factor, is the curvature factor, is the amount of horizontal deformation, is a constant, is the amount of ground normal deformation, is a constant, , It is the intermediate variable of the magic formula.

[0078] ;

[0079] In the formula, is the tire vertical force , is a constant determined by the properties of the vehicle's tires.

[0080] In the present invention, as a preferred embodiment:

[0081] ;

[0082] ;

[0083] vertical force Calculated by the following formula:

[0084] ;

[0085] In the formula, is the acceleration due to gravity, They are the vertical force of the left front tire, the vertical force of the right front tire, the vertical force of the left rear tire, and the vertical force of the right rear tire.

[0086] According to the characteristics of distributed wire-controlled intelligent vehicles , the trajectory tracking nonlinear model is obtained as follows:

[0087] ;

[0088] In the formula, is the nonlinear expression calculated by the above two formulas, is the yaw angular acceleration, is the front wheel turning angle.

[0089] Since the signal has a hysteresis effect in the process of transmission from the controller to the actuator, the communication delay has a positive effect on the trajectory tracking control accuracy. , They are the delay time from the controller to the left front wheel, the delay time from the controller to the right front wheel, the delay time from the controller to the left rear wheel, and the delay time from the controller to the right rear wheel, in units of .

[0090] In the present invention, as a preferred embodiment, , thus the yaw angular velocity Side slip angle with center of mass Re-expressed as and , the trajectory tracking model can be updated as follows:

[0091] ;

[0092] In the formula, For no delay time, Delay time for the left front wheel, is the delay time of the left rear wheel and the delay time of the right rear wheel, is the yaw angular acceleration without delay, For left front wheel Directional force, is the delayed sideslip angle of the left front wheel, is the delayed sideslip angle of the left rear front wheel, is the delayed yaw rate of the left front wheel, is the delayed yaw velocity of the left rear wheel, is the undelayed yaw rate, is the distance from the center of mass to the front axle, For left rear wheel Directional force, For right rear wheel Directional force, is the distance from the center of mass to the rear axle, is the front wheel turning angle, is the vehicle mass, For vehicle speed Axis weight For vehicles around The moment of inertia of the shaft rotation.

[0093] This is achieved by controlling the front wheel angle To control the vehicle's yaw rate Side slip angle with center of mass , thereby achieving the purpose of controlling lateral displacement.

[0094] The nonlinear trajectory tracking controller considering communication delay includes:

[0095] According to the model in the above formula, the lateral velocity can be obtained , then the lateral displacement , the lateral error of trajectory tracking is ,in As a reference trajectory, this example uses the classic double lane change condition.

[0096] Setting output constraints , which is used to constrain the output range of the controller and constrain the lateral displacement to Set the helper function as follows:

[0097] ;

[0098] In the formula, is the maximum lateral displacement, is the minimum lateral displacement;

[0099] From the output constraints we can get:

[0100] ;

[0101] In the formula, is the maximum lateral displacement, is the minimum lateral displacement, is the reference lateral displacement, is the lateral error;

[0102] Use the inverse adjustment function:

[0103] ;

[0104] In the formula, is the maximum reverse regulation function, is the minimum inverse regulation function;

[0105] Use penalty functions to limit ,set up Is the constant to be optimized:

[0106]

[0107] In the formula, is the penalty function;

[0108] Set up the helper function:

[0109] ;

[0110] In the formula, is the constant to be optimized;

[0111] Setting function:

[0112] ;

[0113] In the formula, Requirements: , and its derivative Need to meet ,Pick , Auxiliary convergence index function, for The derivative of

[0114] The final control amount as follows:

[0115] ;

[0116] Determine the parameters to be optimized , , , iteratively optimized by the BEO algorithm.

[0117] like Figure 4-6 As shown in the figure, the controller has significant advantages over the traditional PID controller error root mean square value of 0.1481 and the MPC controller error root mean square value of 0.0856, which verifies the superiority and feasibility of the controller.

[0118] The BEO algorithm includes:

[0119] Initialize

[0120] First, set the population size to , the dimension of the variable to be optimized is , , is the black hawk population matrix to be optimized, , Represents different black hawk individuals to be optimized:

[0121] ;

[0122] ;

[0123] In the formula, For each column of the population matrix, that is, different black hawk individuals, To optimize the upper bound of the variable, To optimize the lower bound of the variable, is a column vector of dimension 3, whose elements are random values ​​from 0 to 1. The fitness value of an individual Indicates the fitness value in the process of "tracking", "circling", "capturing", "snatching", "migration", "courtship" and "hatching". In the present invention, the fitness calculation method is consistent with the performance index same;

[0124] Iterative optimization based on BEO algorithm, the black hawk population matrix to be optimized After: tracking, hovering, capturing, snatching, warning, migrating, courting, hatching, the optimal solution obtained in the last iteration is selected to meet the controller evaluation index The smallest solution is taken as the optimal solution, and the optimal parameters are obtained. , , ,include:

[0125] track

[0126] ;

[0127] ;

[0128] ;

[0129] In the formula, For the The auxiliary tracking population matrix , is a random position in the search space, is the position of a random individual, is the current best solution set; , for The maximum distance from the search boundary, is the number of iterations; is a random number between 0 and 1. Reason (Tent Map) A random number between 0 and 1 generated, To track updates, For the Random individual positions, For the The optimal solution for the iteration.

[0130] By tracking and calculating the candidate solutions and fitness values , and temporarily record it as the minimum fitness value .

[0131] in, , for any input The corresponding tent map is generated .

[0132] Since the object to be optimized is a minimum problem, the fitness function of the algorithm is the performance index function to be optimized The fitness calculation method is the same as the performance index. is the error vector.

[0133] Hover

[0134] ;

[0135] In the formula, , is a 3D convolution matrix, , is the hovering angle; is a random number between 0 and 1.

[0136] By circling, the candidate solutions and fitness values ​​are calculated. , and replace it with the smaller fitness value And record the candidate solution set.

[0137] 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.

[0138] Capture

[0139] ;

[0140] ;

[0141] In the formula, is the auxiliary matrix, To iterate to The parameter matrix , is the position adjustment factor 1, is the position adjustment factor 2, is a column vector of dimension 3, with elements between 0.5 and 1.

[0142] , .

[0143] By capturing the candidate solutions and the fitness values , and replace it with the smaller fitness value And record the candidate solution set.

[0144] snatch:

[0145] ;

[0146] in, is a random vector with dimension 3, and the values ​​of the elements in each dimension follow the normal distribution.

[0147] By robbing, the candidate solutions and fitness values ​​are calculated. , and replace it with the smaller fitness value And record the candidate solution set.

[0148] The snatching behavior is simplified to a black hawk flying from one point to another in a curve, and then the jumping motion of the point is used to simulate the snatching behavior of the black hawk. The snatching strategy is a jumping search method, which aims to perform a jumping search on the position adjusted by the previous strategy to increase the probability of finding the global optimal position.

[0149] warn:

[0150] ;

[0151] ;

[0152] In the formula, , , , , , It is In dimensional space The distance from the center of the search space, and The smallest and largest , yes Follow the approach The position matrix after rearranging the order of is the boundary auxiliary function, is the sine auxiliary function, For the Warning update auxiliary matrix, Indicates Line List, is the probability density function of the Poisson distribution.

[0153] The candidate solutions and fitness values ​​are calculated through warnings. , and replace it with the smaller fitness value And record the candidate solution set.

[0154] Warning: Use Poisson distribution to guide the movement of particles and generate new Used to replace solutions that exceed the search boundary.

[0155] migrate:

[0156] ;

[0157] in, is the auxiliary migration function, is the current best adaptation value, For the The fitness value of each individual, is a column vector of dimension 3, with elements between -1 and 1, is a random number from 0.4 to 1 formed by the tent map, is a constant.

[0158] The candidate solutions and fitness values ​​are calculated by migration. , and replace it with the smaller fitness value And record the candidate solution set.

[0159] Migration uses the fitness function value to represent the adaptability of the black hawk to the environment, and constructs the migration function 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 best position to reduce the possibility of falling into the local optimum.

[0160] Courtship:

[0161] ;

[0162] in, , is the step size factor, and its function expression comes from the deformation of the Sigmoid function; and is a random number between 0 and 1. and is a column vector of dimension 3 whose elements follow a normal distribution. express Divide by 2 and take the remainder, for example: express Divide by 2 and the remainder is 1, which means Is an odd number.

[0163] The candidate solutions and fitness values ​​are calculated by seeking , and replace it with the smaller fitness value And record the candidate solution set.

[0164] Courtship uses similar fluctuations between sine and cosine functions and alternating patterns between odd and even numbers to simulate the interactive behavior of male and female black hawks during courtship.

[0165] ;

[0166] in, is a set of normally distributed arrays, The group position The group position matrix after rearrangement from closest to farthest from the nest .

[0167] The candidate solutions and fitness values ​​are calculated by incubation. , and replace it with the smaller fitness value And record the candidate solution set.

[0168] The hatching strategy 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 female black eagles' hatching. The hatching 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.

[0169] BEO algorithm process: Initialize at the beginning, set the population size, the dimension of the optimization variable, and the matrix for simulating the black hawk position; is the maximum number of iterations, is the number of black hawk individuals, is the number of times to stop updating (the number of times eliminated during the iteration); after initialization, the current optimal solution is found by tracking and evaluating the fitness value of the population , and temporarily record it as the minimum fitness value ;judge Whether the search boundary is reached, if so: execute the "warning" step to generate a new candidate solution minimum fitness value , to replace the solution that exceeds the boundary; if not: execute the "circling", "capturing" and "snatching" steps to update the minimum fitness value ; Determine the current number of iterations , maximum number of iterations , the number of times the update was stopped Is the relationship between: , if satisfied: execute the "migration" step to update the minimum fitness value ; If not satisfied: execute the "courtship" and "hatching" steps to update the minimum fitness value End the cycle after that; update the minimum fitness value in the "migration" step Then, determine whether it satisfies: , if satisfied: execute the "courtship" and "hatching" steps to update the minimum fitness value Then end the loop; if not satisfied: jump back to initialization and re-enter the loop.

[0170] The BEO algorithm process ends, the iteration is completed, and the BEO algorithm obtains the optimal solution. If more than one optimal solution is obtained, that is, a set of optimal solutions is obtained, only the performance index is taken. The smallest solution is taken as the optimal solution, and the rest are eliminated.

[0171] Simulation Verification

[0172] like Figure 4-6 As shown, the reference working condition is the classic double lane shift working condition, and the parameters are set. , simulation was carried out in MATLAB / Simulink, and parameters such as lateral displacement, lateral error, yaw angular velocity, and lateral error root mean square value were calculated, and compared with traditional PID and MPC control algorithms. The results show that the controller designed in the present invention is superior to traditional PID and MPC controllers in tracking accuracy, and the lateral error root mean square value is 0.0323, which is better than PID's 0.1481 and MPC's 0.0856, proving that the controller designed in the present invention has good performance.

[0173] 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 controlled-by-wire vehicle trajectory tracking control method considering communication delay, characterized in that: include: Step 1: Establish a nonlinear two-degree-of-freedom dynamic model of a distributed controlled-by-wire vehicle based on the magic formula; Step 2: Based on the nonlinear two-degree-of-freedom dynamics model, a nonlinear system trajectory tracking controller with communication delay characteristics is established, including: constraining the lateral displacement to Set the helper function for: ； In the formula, is the estimated convergence time; From the output constraints we can get: ； In the formula, is the maximum lateral displacement, is the minimum lateral displacement, is the reference lateral displacement, is the lateral error; Use the inverse adjustment function: ； In the formula, is the maximum reverse regulation function, is the minimum inverse regulation function; Use penalty functions to limit : ； In the formula, is the constant to be optimized, is the penalty function; Set the helper function to: ； In the formula, is a positive constant to be optimized; Setting function: ； and, Requirements: , its derivative Need to meet , is the auxiliary convergence exponential function, for The derivative of Final control volume for: ； Determine the parameters to be optimized as , , ; Step 3: Establish the error function and the evaluation index of the nonlinear system trajectory tracking controller, and obtain the optimal controller parameters based on the BEO algorithm, including: Establish error function and controller evaluation index: , Set the population size to , the dimension of the variable to be optimized is , , is the black hawk population matrix to be optimized; ； ； In the formula, are different black hawk individuals to be optimized, For each column of the population matrix, that is, different black hawk individuals, and are the lower and upper boundaries of the search space, respectively. Is the dimension A column vector whose elements are random values ​​from 0 to 1; Iterative optimization based on BEO algorithm, the black hawk population matrix to be optimized After: tracking, hovering, capturing, snatching, warning, migrating, courting, hatching, the optimal solution obtained in the last iteration is selected to meet the controller evaluation index The smallest solution is taken as the optimal solution, and the optimal parameters are obtained. , , .

2. The distributed wire-controlled vehicle trajectory tracking control method considering communication delay according to claim 1 is characterized in that: The magic formula is: ； In the formula, is the lateral force, is the slip angle, is the stiffness factor, is the curvature factor, Form Factor, is the peak factor, , is the intermediate variable in the magic formula, is the amount of horizontal deformation, is the amount of ground normal deformation; Based on the magic formula and considering the communication delay, a nonlinear model of trajectory tracking is established: ； In the formula, For no delay time, Delay time for the left front wheel, is the delay time of the left rear wheel and the delay time of the right rear wheel, is the yaw angular acceleration without delay, For left front wheel Directional force, is the delayed sideslip angle of the left front wheel, is the delayed sideslip angle of the left rear front wheel, is the delayed yaw rate of the left front wheel, is the delayed yaw velocity of the left rear wheel, is the undelayed yaw rate, is the distance from the center of mass to the front axle, For left rear wheel Directional force, For right rear wheel Directional force, is the distance from the center of mass to the rear axle, is the front wheel turning angle, is the vehicle mass, For vehicle speed Axis weight For vehicles around The moment of inertia of rotation.

3. The distributed wire-controlled vehicle trajectory tracking control method considering communication delay according to claim 2 is characterized in that: set up is constant ,set up .

Images