An optimization method for electric vehicle steering motor control considering communication delay

Abstract

The invention discloses an electric vehicle steering motor control optimization method considering communication delay, comprising: obtaining an expected value of a motor steering angle, and obtaining expected values ​​of a motor steering angular velocity and a steering angular acceleration according to the expected value of the motor steering angle; inputting the expected values ​​of the motor steering angle, steering angular velocity and steering angular acceleration into a feedforward control module, and the feedforward control module outputs a feedforward control quantity signal; obtaining a motor operation signal through a motor sensor, and inputting the motor operation signal into a delay observer, and the delay observer outputs observation values ​​of the steering angle, steering angular velocity and steering angular acceleration with a time delay; inputting the signals output by the delay observer, the switching function module and the external source interference module into the feedback control module, and the feedback control module outputs a feedback control quantity signal; and adding the feedforward control quantity to the feedback control quantity to obtain an expected current value.

Description

Technical Field

[0001] The present invention belongs to the field of distributed wire-controlled intelligent vehicle steering-by-wire, and in particular relates to an electric vehicle steering motor control optimization method taking communication delay into consideration. Background Art

[0002] In the context of the de-transmission of automobile chassis, the wire-controlled chassis technology is gradually replacing the traditional chassis. As one of the core technologies of the wire-controlled chassis, wire-controlled steering can eliminate the complex and huge mechanical structure of the traditional transmission system, improve transmission efficiency and response time, and ensure the stability of the car.

[0003] On this basis, since there is a delay in the signal reaching the actuator from the sensor, this physical delay will affect the performance of the actuator to a certain extent; the traditional steering motor control strategy adopts a three-closed-loop PID control method, and the traditional method uses PID control in the position loop, speed loop, and current loop. Summary of the invention

[0004] The purpose of the present invention is to provide an electric vehicle steering motor control optimization method taking into account communication delay. By introducing a delay observer and a robust adaptive sliding mode method combining feedforward control with feedback control, the disadvantages caused by signal delay are overcome and the stability and accuracy of steering control are improved.

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

[0006] A method for optimizing electric vehicle steering motor control considering communication delay, comprising:

[0007] Obtaining an expected value of the motor steering angle from the upper controller, and obtaining expected values ​​of the motor steering angular velocity and steering angular acceleration according to the expected value of the motor steering angle;

[0008] Inputting the expected values ​​of the steering angle, steering angular velocity and steering angular acceleration of the motor into a feedforward control module of a speed loop controller, and the feedforward control module outputs a feedforward control quantity signal;

[0009] The feedforward control module obtains the feedforward control quantity signal through the following formula:

[0010]

[0011] in: is the feedforward control signal; is the friction viscosity coefficient; is the motor's rotational inertia, which comes from the motor sensor; The steering motor load torque comes from the motor sensor; Motor torque coefficient; , is the expected value of the given motor steering angle and motor steering angular velocity, which comes from the driver's driving needs. for The two components of , is the expected value of the given motor steering angular velocity and motor steering angular acceleration, which comes from the driver's driving requirements. for The two components of is the expected value of the motor steering angle; is the expected value of the motor steering angular velocity; is the expected value of the motor steering angular acceleration;

[0012] Obtain motor operation signals through motor sensors;

[0013] A delay observer is arranged at the rear end of the lower controller and the actuator; the motor operation signal is input into the delay observer, and the delay observer outputs the observed values ​​of the steering angle, steering angular velocity and steering angular acceleration with time delay;

[0014] The speed loop controller also includes: a switching function module, an external interference module and a feedback control module; the expected value and the observed value of the steering angle, steering angular velocity and steering angular acceleration of the motor are input into the switching function module and the feedback control module; the signal output by the switching function module is input into the external interference module;

[0015] Input the signals output by the delay observer, the switching function module and the external interference module into the feedback control module, and the feedback control module outputs a feedback control quantity signal;

[0016] The feedback control module obtains the feedback control amount signal through the following formula:

[0017]

[0018]

[0019] in: is the feedback control quantity signal; is the switching function; is the error between the observed value and the expected value of the motor steering angle; for The derivative of is the defined auxiliary variable; is the coefficient of the switching function in the speed loop controller and is the parameter to be optimized; The coefficients in the auxiliary variable definition process of the speed loop controller are the parameters to be optimized; is the sliding mode coefficient in the speed loop controller and is the parameter to be optimized; is the switching term coefficient; Motor parameter perturbation and external interference; For motor parameter perturbations and external disturbances An estimated value of is a symbolic function; is the optimization function; , is the observed value of the motor steering angle and motor steering angular velocity output by the delay observer, for The two components of is the motor steering angle, which comes from the motor sensor; About Function of is the motor steering angular velocity; About Function of is the motor steering angular acceleration; is the observed value of the motor steering angle output by the delay observer; is the observed value of the motor steering angular velocity output by the delay observer;

[0020] Optimizing the feedback control quantity signal by establishing performance indicators and error functions;

[0021] The performance indicators are:

[0022]

[0023] The error function is:

[0024]

[0025] in: is a performance indicator; is the error function; There is no delay time;

[0026] Taking the minimum performance index as the optimization goal, the parameters , and Optimize and get the optimal parameters , and To adjust the feedback control signal , obtaining the feedback control quantity signal;

[0027] Adding the feedforward control quantity signal and the feedback control quantity signal to obtain a current expected value;

[0028] The lower-level controller and the actuator receive the expected current value and perform a steering operation.

[0029] Preferably, the process of optimizing the feedback control amount signal further includes:

[0030] The constraints are:

[0031]

[0032] in: is the auxiliary matrix.

[0033] Preferably, the calculation formula of the switching function is:

[0034]

[0035] .

[0036] Preferably, an adaptive law is used to estimate the external interference:

[0037]

[0038] in: for The derivative of is the adaptive estimation coefficient.

[0039] Preferably, the delay observer state space expression is designed as:

[0040]

[0041] in: , is the observed value of the motor steering angular velocity and motor steering angular acceleration output by the delay observer, for The two components of is the observed value of the motor steering angular acceleration output by the delay observer; is the coefficient matrix of the state space expression of the delay observer; is the output matrix; is the observer gain matrix; is a constant; To output a delayed signal; The steering angle value with time delay fed back by the motor encoder; is the time-varying delay time; is the observed value of the delayed signal; is the motor output torque, which comes from the motor sensor; is the motor control quantity.

[0042] Preferably, the state space equation of the delay observer with respect to the speed loop controller is:

[0043]

[0044] in: is the coefficient matrix of the state space equation of the speed loop controller; To control the amount; is the expected value of current.

[0045] The beneficial effects of the present invention are:

[0046] The electric vehicle steering motor control optimization method considering communication delay provided by the present invention, on the basis of the traditional three-closed-loop PID, introduces a delay observer at the actuator end, monitors and processes signal delay in real time, and effectively improves the control accuracy of the steering motor; adopts a robust adaptive sliding mode method combining feedforward control and feedback control to control the speed loop controller, overcomes various interference factors, and significantly enhances the robustness and stability of the system; optimizes the parameters of the speed loop controller to ensure the optimality of the speed loop controller parameters and improves the control performance; the designed speed loop controller model is simple and efficient, which improves the processing speed of the speed loop controller and reduces the system complexity. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a flow chart of the electric vehicle steering motor control optimization method considering communication delay according to the present invention.

[0048] Figure 2 This is a flow chart of the speed loop controller described in the present invention.

[0049] Figure 3 This is a flow chart of the BEO algorithm described in the present invention. DETAILED DESCRIPTION

[0050] 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.

[0051] like Figure 1 As shown, the present invention provides an electric vehicle steering motor control optimization method considering communication delay, including:

[0052] According to the working principle and overall control architecture of the steering system of distributed wire-controlled vehicles, a delay observer for time-varying delay signals is designed to deal with the hazards caused by delay signals; the delay observer is set at the back end of the lower-level controller and the actuator to monitor the signal delay in real time and determine the actual state of the steering motor to feed back to the speed loop controller; a speed loop control model including electromechanical coupling interference and delay signal interference is constructed, and a robust adaptive sliding mode method combining feedforward control and feedback control is used to control the speed loop controller; the desired steering angle of the steering motor is obtained from the upper-level controller; performance indicators, error functions and constraints are established, and the BEO algorithm is used to calculate the speed loop control model. The optimal speed loop controller parameters are obtained by iterative optimization; the lower-level controller and actuator receive the expected current value and perform steering operations; the speed loop controller integrates the delay signal and external interference provided by the delay observer, calculates and outputs the expected current value, so that the steering angle expected value provided by the upper-level controller for the steering motor is converted into the expected current value required by the lower-level controller and the actuator, and the lower-level controller and the actuator perform steering operations according to the steering angle expected value of the upper-level controller; the upper-level controller transmits the steering wheel to the VCU hardware, and provides the expected value of the motor steering angle according to the driver's intention, and the lower-level controller and the actuator are the motor and the wheel.

[0053] like Figure 2 As shown, the present invention provides an electric vehicle steering motor control optimization method considering communication delay, which includes: obtaining an expected value of the motor steering angle, and obtaining the expected values ​​of the motor steering angular velocity and steering angular acceleration according to the expected value of the motor steering angle; inputting the expected values ​​of the motor steering angle, steering angular velocity and steering angular acceleration into a feedforward control module, and the feedforward control module outputs a feedforward control quantity signal; obtaining a motor operation signal through a motor sensor, and inputting the motor operation signal into a delay observer, and the delay observer outputs the observed values ​​of the steering angle, steering angular velocity and steering angular acceleration with time delay; inputting the expected value and observed value of the motor steering angle, steering angular velocity and steering angular acceleration into a switching function module and a feedback control module; the signal output by the switching function module is input into an external interference module; the signals output by the delay observer, the switching function module and the external interference module are input into the feedback control module, and the feedback control module outputs a feedback control quantity signal; and the feedforward control quantity is added to the feedback control quantity to obtain the expected current value.

[0054] The present invention provides an electric vehicle steering motor control optimization method considering communication delay. According to the working principle of the steering system of a distributed wire-controlled vehicle and the overall control architecture, a delay observer for time-varying delay signals is designed, and a speed loop control model is constructed, which includes electromechanical coupling interference and delay signal interference. A robust adaptive sliding mode method combining feedforward control and feedback control is used to control the speed loop controller instead of the traditional PID method, and an adaptive law is used to monitor external interference in real time, so that the speed loop controller adjusts the control output according to the monitoring result of the adaptive law module. Based on the robust sliding mode control, various interferences are overcome and control performance optimization is achieved.

[0055] Design of delay observer:

[0056] In a distributed wire-controlled vehicle steering system, the signal goes through the sensor, CAN bus, and controller to reach the actuator, and the physical delay will greatly weaken the effectiveness of the actuator.

[0057] To this end, the present invention proposes a time-varying delay observer to monitor the signal delay between the controller, the motor encoder and the CAN communication network in real time, and shields the harm caused by the delay characteristics by processing the observed signal in the underlying controller.

[0058] In the steering actuator, the core driving unit is the permanent magnet servo motor. Based on the working principle and electromechanical coupling characteristics of the permanent magnet servo motor used in the distributed wire-controlled vehicle steering system, its mechanical characteristic equation can be obtained:

[0059]

[0060] in:

[0061] Output torque for the steering motor, from the motor sensor, as control input;

[0062] The steering motor load torque comes from the motor sensor;

[0063] The moment of inertia of the steering motor comes from the motor sensor;

[0064] is the friction viscosity coefficient;

[0065] There is no delay time;

[0066] is the motor steering angle, which comes from the motor sensor; About Function of

[0067] is the motor steering angular velocity; About Function of

[0068] is the motor steering angular acceleration.

[0069] The state space expression for the motor steering angle is:

[0070]

[0071]

[0072] in:

[0073] , is the state variable, for The two components of

[0074] , for The derivative of for The two components of

[0075] Output torque for the steering motor, from the motor sensor, as control input;

[0076] To output a delayed signal as a control output;

[0077] The steering angle value with time delay fed back by the motor encoder;

[0078] is the time-varying delay time;

[0079] is the coefficient matrix of the state space expression for the motor steering angle.

[0080] Based on the state space expression of the motor steering angle, the state space expression of the delay observer is designed as:

[0081]

[0082]

[0083] in:

[0084] , The observed values ​​of the motor steering angle and motor steering angular velocity output by the delay observer, for The two components of is the observed value of the motor steering angle output by the delay observer; is the observed value of the motor steering angular velocity output by the delay observer;

[0085] , is the observed value of the motor steering angular velocity and motor steering angular acceleration output by the delay observer, for The two components of is the observed value of the motor steering angular acceleration output by the delay observer;

[0086] is the coefficient matrix of the state space expression of the delay observer;

[0087] is the output matrix;

[0088] is the observer gain matrix;

[0089] is a constant;

[0090] is the observed value of the delayed signal;

[0091] is the motor control quantity;

[0092] By controlling To change , used to observe Follow of the changing relationship.

[0093] The condition for the delay observer state space expression to converge is: Satisfies the Hurwitz condition, where: is the convergence matrix;

[0094] The characteristic equation is:

[0095]

[0096] in:

[0097] is the characteristic value;

[0098] is the unit diagonal matrix;

[0099] By adjusting Thus adjusting ,make Satisfy the Hurwitz convergence condition and ensure the stability of the system.

[0100] Design of speed loop controller:

[0101] The goal of the speed loop controller is to overcome the degradation of control performance caused by signal transmission delay, external input interference and internal parameter perturbation, and to ensure the robustness and accuracy of the steering actuator.

[0102] The mechanical characteristics of the steering actuator are affected by external interference and assembly errors. The moment of inertia and viscosity coefficient will change. The perturbation of these parameters can be expressed in the mechanical characteristic equation:

[0103]

[0104] in:

[0105] Represents the perturbation value of the steering motor's rotational inertia;

[0106] is the perturbation value of the friction viscosity coefficient;

[0107] Enter the disturbance value for the outside world.

[0108] The delay observer establishes the state space expression of the speed loop controller as follows:

[0109]

[0110] in:

[0111] is the motor torque coefficient;

[0112] To control the amount;

[0113] is the expected value of current;

[0114] is the feedforward control signal;

[0115] is the feedback control quantity signal;

[0116] Motor parameter perturbation and external interference;

[0117] is the coefficient matrix of the state space expression of the delay observer with respect to the speed loop controller.

[0118] Representing the perturbation in the mechanical characteristic equation yields:

[0119]

[0120] The state space expression for the desired value of the motor steering angle is given by:

[0121]

[0122] in:

[0123] , is the expected value of the given motor steering angle and motor steering angular velocity, which comes from the driver's driving needs. for The two components of

[0124] , is the expected value of the given motor steering angular velocity and motor steering angular acceleration, which comes from the driver's driving requirements. for The two components of

[0125] is the expected value of the motor steering angle;

[0126] is the expected value of the motor steering angular velocity;

[0127] is the expected value of the motor steering angular acceleration;

[0128] is the coefficient matrix of the state-space expression for the desired value of the motor steering angle.

[0129] Furthermore, the feedforward control module in the speed loop controller obtains the feedforward control quantity signal through the following formula:

[0130]

[0131] In order to further overcome the interference of factors such as signal transmission delay and parameter perturbation, it is also necessary to solve the feedback control quantity. Substitute the feedforward control quantity in the speed loop controller into the delay observer to establish the state space expression of the speed loop controller, and obtain the motor steering angle observation value and feedback control quantity. The state space expression of is:

[0132]

[0133] in:

[0134] is the motor steering angle observation value and feedback control quantity The coefficient matrix of the state-space expression of .

[0135] Setting auxiliary variables :

[0136]

[0137]

[0138] in:

[0139] is the defined auxiliary variable;

[0140] is the error between the observed value and the expected value of the motor steering angle;

[0141] Let the first Lyapunov function be :

[0142]

[0143] right The derivative is :

[0144]

[0145] in:

[0146] for The derivative of

[0147] for The derivative of

[0148] Based on Lyapunov's stability principle, , then you need and Converges to 0.

[0149] Set the switch function to , the calculation formula of the switching function is:

[0150]

[0151]

[0152] is the switching function;

[0153] is the coefficient of the switching function in the speed loop controller and is the parameter to be optimized;

[0154] After the expected values ​​and observed values ​​of the steering angle, steering angular velocity and steering angular acceleration of the motor are input into the switching function module, a switching function signal will be obtained. The switching function signal is a smooth curve, which makes the feedback sliding mode control smoother; by changing the value of the control parameter, the system can maintain excellent control performance under different working conditions.

[0155] Let the second Lyapunov function be :

[0156]

[0157]

[0158] in:

[0159] is the interference convergence speed coefficient;

[0160] For motor parameter perturbations and external disturbances An estimated value of

[0161] is the interference estimation error;

[0162] right The derivative is :

[0163]

[0164]

[0165] in:

[0166] for The derivative of

[0167] for The derivative of

[0168] for The derivative of

[0169] for The derivative of

[0170] for The derivative of

[0171] for The derivative of

[0172] In the case of slow external interference, the visual .

[0173] Substituting the above conclusion into In the expression of , we get:

[0174]

[0175] The feedback control module in the speed loop controller obtains the feedback control quantity signal through the following formula:

[0176]

[0177]

[0178] in:

[0179] is a symbolic function;

[0180] The coefficients in the auxiliary variable definition process in the speed loop controller are the parameters to be optimized;

[0181] is the sliding mode term coefficient in the speed loop controller and is the parameter to be optimized;

[0182] is the switching term coefficient;

[0183] is the optimization function;

[0184] Set the following reaching law optimization function:

[0185]

[0186]

[0187] in:

[0188] is the sliding mode function;

[0189] for The derivative of

[0190] Indicates absolute value;

[0191] is a natural constant;

[0192] is the reaching law coefficient;

[0193] is the optimization function for the reaching law;

[0194] is a constant;

[0195] about The optimization function for:

[0196]

[0197] Adopting adaptive law to deal with the external interference Make an estimate:

[0198]

[0199] in:

[0200] is the adaptive estimation coefficient;

[0201] External interference refers to interference signals received by the motor from outside the motor that affect the motor. Sensor signals fluctuate due to hardware physical reasons, and the external environment affects signal acquisition. For example, static electricity affects sensor accuracy.

[0202] The second differential Lyapunov function of this system is:

[0203]

[0204] Let auxiliary matrix for:

[0205]

[0206] Based on the auxiliary matrix You can get:

[0207]

[0208] Let the auxiliary vector be ;

[0209]

[0210] in:

[0211] for The transpose of

[0212] If you is a positive definite matrix, which satisfies .

[0213] The output variable of the speed loop controller is used as the input to the lower controller and the current expected value of the actuator , through the lower-level controller and actuator, thereby updating the output of the actuator and the output of the delay observer .

[0214] Establish performance indicators and error functions to optimize the feedback control module:

[0215]

[0216]

[0217] in:

[0218] is a performance indicator;

[0219] is the error function;

[0220] About function.

[0221] Iterative optimization of speed loop controller parameters using BEO algorithm , and , satisfying the constraints and is a positive definite matrix, and the parameters of the optimized speed loop controller are obtained , and , after optimization , and To adjust the feedback signal Then change the control quantity in the state space expression of the delay observer with respect to the speed loop controller , adjust the state quantity through the control quantity , Achieve the goal of controlling steering accuracy.

[0222] like Figure 3 As shown in the figure, the BEO algorithm builds a mathematical model based on the behavior of the black hawk, performs iterative optimization through tracking, circling, capturing, snatching, warning, migration, courtship and hatching, and finds the optimal solution in a large range of iteration space. Initialization is used to set the population size, the dimension of the optimization variable and the matrix of the simulated black hawk position. Judgment is used to judge the relationship between the current number of iterations, the maximum number of iterations and the number of stop updates, and execute the corresponding update steps according to the judgment result.

[0223] In the present invention, for the performance index, the BEO algorithm is used for iterative optimization to find the optimal solution in a large range of iteration space.

[0224] initialization:

[0225] First, set the population size to , the dimension of the variable to be optimized is , , is the black hawk population matrix:

[0226]

[0227]

[0228] in:

[0229] Represents different black hawk individuals to be optimized

[0230] Each column of the population matrix is ​​a different black hawk individual;

[0231] is the upper bound of the optimization variable;

[0232] is the lower bound of the optimization variable;

[0233] is a column vector of dimension 3, whose elements are random values ​​from 0 to 1;

[0234] is the fitness value of the individual;

[0235] In the present invention, .

[0236] track:

[0237]

[0238]

[0239]

[0240]

[0241] in:

[0242] For the The auxiliary tracking population matrix ;

[0243] For the Sub-tracking population matrix ;

[0244] For the Sub-tracking population matrix ;

[0245] is a random position in the search space;

[0246] For the A random position in the secondary search space;

[0247] is the position of a random individual;

[0248] For the Secondary random individual position;

[0249] is the current best solution set;

[0250] For the The optimal solution of the iteration;

[0251] for Maximum distance from the search boundary;

[0252] is the number of iterations;

[0253] A random number between 0 and 1;

[0254] Reason (Tent Mapping) A random number between 0 and 1 generated;

[0255] Helper functions for tracking updates.

[0256] By "tracking", the candidate solutions and fitness values ​​are calculated. , and temporarily record it as the current best adaptation value .

[0257] in:

[0258]

[0259] For any input The corresponding tent map is generated .

[0260] Hover:

[0261]

[0262]

[0263] in:

[0264] is a 3D convolution matrix;

[0265] is the circling angle;

[0266] is a random number between 0 and 1.

[0267] By "circling", the candidate solutions and fitness values ​​are calculated. , and replace it with the smaller fitness value And record the candidate solution set.

[0268] The function of the "circling" operation is to perform a rotation search to further improve the range of the global optimal position. The "circling" strategy performs a rotation search within the range of the global optimal position initially determined by the tracking strategy.

[0269] Snap:

[0270]

[0271]

[0272]

[0273] in:

[0274] is the auxiliary matrix;

[0275] For the Capture and update the auxiliary matrix;

[0276] is the position adjustment factor 1;

[0277] is the position adjustment factor 2;

[0278] is a column vector of dimension 3, with elements between 0.5 and 1.

[0279] By "capturing", the candidate solutions and fitness values ​​are calculated. , and replace it with the smaller fitness value And record the candidate solution set.

[0280] snatch:

[0281]

[0282] in, is a random vector with dimension 3, and the values ​​of the elements in each dimension follow the normal distribution.

[0283] By "snatching", the candidate solutions and fitness values ​​are calculated. , and replace it with the smaller fitness value And record the candidate solution set.

[0284] The "grabbing" 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 grabbing behavior of the black hawk. The "grabbing" strategy is a jumping search method, which aims to perform jumping search on the position adjusted by the previous strategy to increase the probability of finding the global optimal position.

[0285] warn:

[0286]

[0287]

[0288]

[0289] in:

[0290] For the In dimensional space The distance from the center of the search space;

[0291] For the smallest ;

[0292] For the largest ;

[0293] is the weighted displacement;

[0294] yes Follow the approach The position matrix after rearrangement of the order;

[0295] For the Second warning updates the auxiliary matrix;

[0296] is the boundary auxiliary function;

[0297] is the first sine auxiliary function;

[0298] is the second sine auxiliary function;

[0299] Indicates Line List;

[0300] is the probability density function of the Poisson distribution.

[0301] The candidate solutions and fitness values ​​are calculated through "Warning" , and replace it with the smaller fitness value And record the candidate solution set.

[0302] "Warning" uses Poisson distribution to guide the movement of particles and generate new Used to replace solutions that exceed the search boundary.

[0303] migrate:

[0304]

[0305]

[0306] in:

[0307] is the auxiliary migration function;

[0308] is the current best adaptation value;

[0309] For the The fitness value of each individual;

[0310] is a column vector with dimension 3, and its elements are between -1 and 1;

[0311] is a random number from 0.4 to 1 formed by the tent map;

[0312] is a constant.

[0313] The candidate solutions and fitness values ​​are calculated by "migration" , and replace it with the smaller fitness value And record the candidate solution set.

[0314] "Migration" uses the fitness function value to represent the adaptability of the black hawk to the environment, and constructs a 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.

[0315] Courtship:

[0316]

[0317]

[0318] in:

[0319] For the Second courtship updates the auxiliary matrix;

[0320] For the Second courtship updates the auxiliary matrix;

[0321] is the step size factor, and its function expression comes from the deformation of the Sigmoid function;

[0322] and A random number between 0 and 1;

[0323] and is a column vector with dimension 3, whose elements follow normal distribution;

[0324] is the maximum number of iterations;

[0325] 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.

[0326] The candidate solutions and fitness values ​​are calculated through "courtship" , and replace it with the smaller fitness value And record the candidate solution set.

[0327] “Courting” 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 the courtship process.

[0328] incubation:

[0329]

[0330] in:

[0331] is a set of normally distributed arrays;

[0332] The group position The group position matrix after rearrangement from closest to farthest from the nest .

[0333] The candidate solutions and fitness values ​​are calculated by "incubation" , and replace it with the smaller fitness value And record the candidate solution set.

[0334] "Incubation" 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 eagle's incubation 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.

[0335] 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; set is the maximum number of iterations, is the number of black hawk individuals, is the number of times to stop updating; after initialization, the fitness value of the population is evaluated by "tracking" to find the current optimal solution , and temporarily record it as the current best adaptation value ;judge Has the search boundary been reached? If so: execute the "Warning" step to generate a new candidate solution and the current best fit value , to replace the solution that exceeds the boundary; if not: execute the "circling", "capturing", and "grabbing" steps to update the current best fit 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 current best fit value ; If not satisfied: execute the "courtship" and "hatching" steps to update the current best adaptation value Then end the cycle; execute the "migration" step to update the current best adaptation value Then, determine whether it satisfies: If satisfied: execute the "courtship" and "hatching" steps to update the current best adaptation value Then end the loop; if not satisfied: jump back to initialization and re-enter the loop.

[0336] The BEO algorithm process ends, the iteration is completed, and the BEO algorithm obtains the optimal solution , and determine whether the optimal solution satisfies and , remove the optimal solution that does not satisfy the above formula and keep the solution that meets the constraints. If the optimal solution is finally obtained If there is more than one set, that is, a set of optimal solutions is obtained, then the one that satisfies the performance index is selected. The smallest solution is taken as the optimal solution.

[0337] in:

[0338] Indicates the motor equivalent resistance, which is the motor's own parameter;

[0339] The performance index that represents the power consumed by the motor is also the economic index.

[0340] The present invention develops an electric vehicle steering motor control optimization method that takes communication delay into consideration. In the traditional motor steering process, the signal delay between the controller and the actuator is usually ignored, which leads to the disadvantage of reduced steering accuracy; or a relatively complicated multi-degree-of-freedom model is established, resulting in over-constraint, making the controller more complex, and prone to long processing time when dealing with complex problems. To this end, based on the traditional three-closed-loop PID, the present invention introduces a delay observer at the actuator end to monitor and process signal delays in real time, effectively improving the control accuracy of the steering motor; adopts a feedforward + feedback adaptive robust sliding mode control method to overcome various interference factors and significantly enhance the robustness and stability of the system; uses the BEO algorithm for parameter optimization to ensure the optimality of the controller setting parameters, further improving the control performance; the designed controller model is simple and efficient, which improves the processing speed of the controller and reduces the complexity of the system.

[0341] 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 method for optimizing electric vehicle steering motor control considering communication delay, characterized in that: include: Obtaining an expected value of the motor steering angle from the upper controller, and obtaining expected values ​​of the motor steering angular velocity and steering angular acceleration according to the expected value of the motor steering angle; Inputting the expected values ​​of the steering angle, steering angular velocity and steering angular acceleration of the motor into a feedforward control module of a speed loop controller, and the feedforward control module outputs a feedforward control quantity signal; The feedforward control module obtains the feedforward control quantity signal through the following formula: in: is the feedforward control signal; is the friction viscosity coefficient; is the motor's rotational inertia, which comes from the motor sensor; The steering motor load torque comes from the motor sensor; Motor torque coefficient; , is the expected value of the given motor steering angle and motor steering angular velocity, which comes from the driver's driving needs. for The two components of , is the expected value of the given motor steering angular velocity and motor steering angular acceleration, which comes from the driver's driving requirements. for The two components of is the expected value of the motor steering angle; is the expected value of the motor steering angular velocity; is the expected value of the motor steering angular acceleration; Obtain motor operation signals through motor sensors; A delay observer is arranged at the rear end of the lower controller and the actuator; the motor operation signal is input into the delay observer, and the delay observer outputs the observed values ​​of the steering angle, steering angular velocity and steering angular acceleration with time delay; The speed loop controller also includes: a switching function module, an external interference module and a feedback control module; the expected value and the observed value of the steering angle, steering angular velocity and steering angular acceleration of the motor are input into the switching function module and the feedback control module; the signal output by the switching function module is input into the external interference module; Input the signals output by the delay observer, the switching function module and the external interference module into the feedback control module, and the feedback control module outputs a feedback control quantity signal; The feedback control module obtains the feedback control amount signal through the following formula: in: is the feedback control quantity signal; is the switching function; is the error between the observed value and the expected value of the motor steering angle; for The derivative of is the defined auxiliary variable; is the coefficient of the switching function in the speed loop controller and is the parameter to be optimized; The coefficients in the auxiliary variable definition process of the speed loop controller are the parameters to be optimized; is the sliding mode coefficient in the speed loop controller and is the parameter to be optimized; is the switching term coefficient; Motor parameter perturbation and external interference; For motor parameter perturbations and external disturbances An estimated value of is a symbolic function; is the optimization function; , is the observed value of the motor steering angle and motor steering angular velocity output by the delay observer, for The two components of is the motor steering angle, which comes from the motor sensor; About Function of is the motor steering angular velocity; About Function of is the motor steering angular acceleration; is the observed value of the motor steering angle output by the delay observer; is the observed value of the motor steering angular velocity output by the delay observer; Optimizing the feedback control quantity signal by establishing performance indicators and error functions; The performance indicators are: The error function is: in: is a performance indicator; is the error function; There is no delay time; Taking the minimum performance index as the optimization goal, the parameters , and Optimize and get the optimal parameters , and To adjust the feedback control signal , obtaining the feedback control quantity signal; Adding the feedforward control quantity signal and the feedback control quantity signal to obtain a current expected value; The lower-level controller and the actuator receive the expected current value and perform a steering operation.

2. The electric vehicle steering motor control optimization method considering communication delay according to claim 1 is characterized in that: The process of optimizing the feedback control amount signal also includes: The constraints are: in: is the auxiliary matrix.

3. The electric vehicle steering motor control optimization method considering communication delay according to claim 1 is characterized in that: The calculation formula of the switching function is: 。 4. The electric vehicle steering motor control optimization method considering communication delay according to claim 3 is characterized in that: The external interference is estimated using an adaptive law: in: for The derivative of is the adaptive estimation coefficient.

5. The electric vehicle steering motor control optimization method considering communication delay according to claim 1 is characterized in that: The delay observer state space expression is designed as: in: , is the observed value of the motor steering angular velocity and motor steering angular acceleration output by the delay observer, for The two components of is the observed value of the motor steering angular acceleration output by the delay observer; is the coefficient matrix of the state space expression of the delay observer; is the output matrix; is the observer gain matrix; is a constant; To output a delayed signal; The steering angle value with time delay fed back by the motor encoder; is the time-varying delay time; is the observed value of the delayed signal; is the motor output torque, which comes from the motor sensor; is the motor control quantity.

6. The electric vehicle steering motor control optimization method considering communication delay according to claim 5 is characterized in that: The state space equation of the delay observer with respect to the speed loop controller is: in: is the coefficient matrix of the state space equation of the speed loop controller; To control the amount; is the expected value of current.

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