Steer-by-wire control system variable transmission ratio parameter optimization method

Abstract

The invention discloses a variable transmission ratio parameter optimization method for a steer-by-wire control system, which comprises the following steps of: designing a variable transmission ratio controller based on fuzzy control, taking a vehicle speed and a steering wheel angle as input variables, taking a transmission ratio as an output variable, performing fuzzification processing on the input and output variables by adopting a Gaussian membership function, and designing a fuzzy rule; carrying out defuzzification by adopting a gravity center method, and converting fuzzy output into a specific transmission ratio numerical value; parameters of the fuzzy controller serve as optimization objects, the parameters of the fuzzy controller are optimized through a simulated annealing algorithm, an initial solution is an initial value of the parameters of the fuzzy controller, steering response, stability and driving comfort of a vehicle are comprehensively considered, an energy function is designed, the performance of the fuzzy controller is evaluated, and the optimal parameters are searched through iteration. And minimizing the energy function to obtain the optimal parameter of the fuzzy controller. The method does not depend on vehicle model precision and expert experience, does not need manual parameter adjustment, has interpretability, and accords with the steering expectation of a driver.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle steer-by-wire systems, and particularly to a method for optimizing variable transmission ratio parameters of a steer-by-wire control system. By using the fuzzy control principle and the simulated annealing algorithm to optimize the controller parameters, more steering control that meets the driver's expectations is achieved. Background Art

[0002] As one of the key technologies of intelligent vehicles, the steer-by-wire (SBW) system plays an important role in improving driving performance and vehicle handling. Its core lies in transmitting the driver's steering intention through electronic signals, replacing the traditional mechanical connection, and achieving more flexible and precise steering control. During actual driving, vehicles face complex and variable working conditions, such as high-speed driving, low-speed turning, and emergency avoidance. Different working conditions have different requirements for the steering transmission ratio. When driving at high speed, a larger transmission ratio is required to ensure the driving stability of the vehicle and avoid vehicle out of control due to excessive steering; while in low-speed driving and parking scenarios, a smaller transmission ratio is needed to enable the driver to easily turn the steering wheel and improve the vehicle's maneuverability. Therefore, a reasonable variable transmission ratio strategy is crucial, directly affecting the steering response and handling stability of the vehicle under different driving conditions.

[0003] The design of previous variable transmission ratio strategies can be summarized into three types: 1) based on fixed gains, such as yaw rate gain and lateral acceleration gain; 2) based on special functions, such as exponential functions, S-shaped functions, etc.; 3) based on intelligent control algorithms, such as fuzzy control, neural networks, reinforcement learning, etc. Although existing research has achieved certain results in improving vehicle handling stability, there are still some problems, such as high requirements for model accuracy, dependence on expert experience and complex parameter tuning, lack of interpretability, and the need for a large amount of data sets for training and verification. Therefore, how to design a variable transmission ratio control strategy that does not depend on vehicle model accuracy, expert experience, does not require manual parameter tuning, and has interpretability and meets the driver's steering expectations has become an urgent problem to be solved. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of the present invention is to provide a method for optimizing variable transmission ratio parameters of a steer-by-wire control system, a variable transmission ratio control strategy that does not depend on vehicle model accuracy, expert experience, does not require manual parameter tuning, and has interpretability and meets the driver's steering expectations.

[0005] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] A method for optimizing variable transmission ratio parameters of a steer-by-wire control system, the steps are as follows:

[0007] Step 1): Design a variable transmission ratio controller based on the fuzzy control principle. The input variables are vehicle speed and steering wheel angle, and the output variable is the transmission ratio. The Gaussian membership function is used to fuzzify the input and output variables, fuzzy rules are designed, and then the centroid method is used for defuzzification to convert the fuzzy output into a specific transmission ratio value.

[0008] Step 2): Take the parameters of the fuzzy controller (including membership function parameters and fuzzy rule parameters) as the optimization objects, and use the simulated annealing algorithm to optimize the parameters of the fuzzy controller. The initial solution is the initial value of the fuzzy controller parameters. Considering the steering response, stability, and driving comfort of the vehicle comprehensively, design an energy function to evaluate the performance of the fuzzy controller, and search for the optimal parameters through iteration to minimize the energy function and obtain the optimal parameters of the fuzzy controller.

[0009] Further, the specific content of step 1) includes:

[0010] This fuzzy controller takes the vehicle speed and the steering wheel angle as input variables, and the transmission ratio of the steer-by-wire system as the output variable.

[0011] The fuzzy set of vehicle speed is divided into "low", "medium", and "high", and its domain is set to [0, 120] km / h; the fuzzy set of the steering wheel angle is divided into "small", "medium", and "large", and its domain is set to [0, 200] degrees; the fuzzy set of the transmission ratio is divided into "small", "medium", and "large", and its domain is set to [5, 30].

[0012] The Gaussian membership function is used to fuzzify the exact values of the input and output variables, and its expression is:

[0013]

[0014] In the formula, x is the variable value to be fuzzified; c is the average value; σ is the standard deviation.

[0015] According to the characteristics of different fuzzy sets, set the corresponding Gaussian membership function parameters for each fuzzy set to accurately describe the membership degree distribution of the fuzzy set.

[0016] Based on the existing research foundation, establish fuzzy control rules.

[0017] Using the Mamdani fuzzy inference method, according to the fuzzy values of the current input vehicle speed and steering wheel angle, combined with the fuzzy control rule base, calculate the fuzzy output set of the transmission ratio.

[0018] The centroid method is used for defuzzification. According to the fuzzy output set, through the calculation formula of the centroid method, calculate the final exact control quantity:

[0019]

[0020] Wherein, m is the number of fuzzy subsets; a and b are the upper and lower limits of the universe of discourse respectively; is the membership function corresponding to each fuzzy subset.

[0021] Furthermore, the specific steps of step 2) include: parameterizing the fuzzy controller and optimizing it using the simulated annealing algorithm:

[0022] Define the membership function parameters as continuous variables and the fuzzy rule parameters as discrete variables.

[0023] Define the parameters of the simulated annealing algorithm: the initial temperature T0, the temperature decay coefficient α, the number of iterations L at each temperature, and the termination temperature T end

[0024] Considering the steering response, stability, and driving comfort of the vehicle comprehensively, design the energy function:

[0025]

[0026] E = α1·j1 + α2·j2 + α3·j3 + α4·j4

[0027] Randomly generate a set of membership function parameters and fuzzy rule parameters to form the initial solution S0.

[0028] Based on this set of parameters, construct the above variable transmission ratio fuzzy controller and perform simulation under the set system model and environment to obtain the initial energy function value E(S0).

[0029] For the outer temperature loop, starting from the initial temperature T0, according to the temperature decay formula:

[0030] T k+1 = α·T k

[0031] Gradually reduce the temperature until the termination temperature T is reached end

[0032] For the inner iteration loop, at each temperature T k perform L iterations.

[0033] Each iteration performs the following steps:

[0034] Randomly generate a new solution S k in the neighborhood of the current solution S new

[0035] Based on the new solution, reconstruct the fuzzy controller and calculate the energy function value E(S new ) and its difference from the energy function value E(S kThe difference ΔE:

[0036] ΔE = E(S new ) - E(S k )

[0037] If ΔE < 0, directly accept the new solution;

[0038] If ΔE ≥ 0, accept the new solution with probability P or keep the current solution.

[0039] When the temperature drops to the termination temperature T end , the simulated annealing algorithm ends. The current solution S final at this time is the optimal fuzzy controller parameter searched.

[0040] According to S final Adjust the membership function parameters and fuzzy rule parameters to obtain an optimized fuzzy controller.

[0041] Advantages of the present invention:

[0042] 1. The present invention provides a design method for a variable transmission ratio fuzzy controller of a steer-by-wire system that optimizes parameters through a simulated annealing algorithm. Existing variable transmission ratio strategies usually have problems such as relying entirely on expert experience, being greatly affected by the accuracy of vehicle models, requiring a large amount of time for manual parameter tuning, lacking interpretability, or not fully meeting the driver's steering expectations. The present invention combines fuzzy control and a simulated annealing algorithm to solve the above technical problems to a certain extent.

[0043] 2. The variable transmission ratio fuzzy controller of the steer-by-wire system optimized by the simulated annealing algorithm of the present invention can dynamically adjust the transmission ratio according to the vehicle speed and the steering wheel angle, optimize the vehicle's steering response and stability, and be more in line with the driver's steering expectations, reducing the physical and mental burden during steering operations.

[0044] The method of the present invention is simple, can effectively improve the control performance of the steer-by-wire system, and provides a new solution for the steering control of intelligent vehicles. Specific embodiments

[0045] For the convenience of those skilled in the art, the present invention will be further described below in conjunction with embodiments. The content mentioned in the embodiments does not limit the present invention.

[0046] A method for optimizing variable transmission ratio parameters of a steer-by-wire control system is as follows:

[0047] Step 1): Design a variable transmission ratio controller based on the fuzzy control principle

[0048] The design of variable transmission ratio needs to obtain the optimal strategy suitable for different driving conditions according to the vehicle dynamics characteristics and the driver's operation requirements, and must meet the low-speed flexibility and high-speed stability.

[0049] Therefore, the variable transmission ratio fuzzy controller proposed by the present invention uses the vehicle speed and the steering wheel angle as input variables, and the transmission ratio of the steer-by-wire system as the output variable.

[0050] The fuzzy set of vehicle speed is divided into "low", "medium", and "high", and the universe of discourse is set to [0, 120] km / h;

[0051] The fuzzy set of the steering wheel angle is divided into "small", "medium", and "large", and the universe of discourse is set to [0, 200] degrees;

[0052] The fuzzy set of the transmission ratio is divided into "small", "medium", and "large", and the universe of discourse is set to [5, 30].

[0053] The Gaussian membership function is used to fuzzify the exact values of the input and output variables, and its expression is:

[0054]

[0055] In the formula, x is the variable value to be fuzzified; c is the average value; σ is the standard deviation.

[0056] According to the characteristics of different fuzzy sets, the corresponding Gaussian membership function parameters are set for each fuzzy set, and the membership degree distribution of the fuzzy set can be accurately described.

[0057] Subsequently, the following fuzzy control rules are established:

[0058] If the vehicle speed is "low" and the steering wheel angle is "small", then the transmission ratio is "medium";

[0059] If the vehicle speed is "low" and the steering wheel angle is "medium", then the transmission ratio is "medium";

[0060] If the vehicle speed is "low" and the steering wheel angle is "large", then the transmission ratio is "low";

[0061] If the vehicle speed is "medium" and the steering wheel angle is "small", then the transmission ratio is "large";

[0062] If the vehicle speed is "medium" and the steering wheel angle is "medium", then the transmission ratio is "large";

[0063] If the vehicle speed is "medium" and the steering wheel angle is "large", then the transmission ratio is "medium";

[0064] If the vehicle speed is "high" and the steering wheel angle is "small", then the transmission ratio is "large";

[0065] If the vehicle speed is "high" and the steering wheel angle is "medium", then the transmission ratio is "large".

[0066] If the vehicle speed is "high" and the steering wheel angle is "large", then the transmission ratio is "large".

[0067] Using the Mamdani fuzzy inference method, based on the fuzzy values of the current input vehicle speed and steering wheel angle, combined with the fuzzy control rule base, calculate the fuzzy output set of the transmission ratio.

[0068] Adopt the centroid method for defuzzification. According to the fuzzy output set, through the calculation formula of the centroid method, calculate the final precise control quantity:

[0069]

[0070] Where m is the number of fuzzy subsets; a and b are the upper and lower limits of the universe of discourse respectively; is the membership function corresponding to each fuzzy subset.

[0071] So far, the design of the variable transmission ratio controller based on the fuzzy control principle is completed.

[0072] Step 2): Parameterize the fuzzy controller designed in Step 1) and optimize its parameters using the simulated annealing algorithm.

[0073] First, define the membership function parameters of the fuzzy controller as continuous variables and the fuzzy rule parameters as discrete variables.

[0074] Secondly, define the parameters of the simulated annealing algorithm: the initial temperature T0 = 10, the temperature decay coefficient α = 0.9, the number of iterations L = 10 at each temperature, and the termination temperature T end = 1

[0075] Taking into account the steering response, stability and driving comfort of the vehicle, design four evaluation indicators:

[0076]

[0077] Where t f is the time of a single test; β is the sideslip angle of the vehicle's center of mass; ω r is the yaw angular velocity of the vehicle; θ sw is the steering wheel angle; e y is the lateral position error when the vehicle performs trajectory tracking. j1 and j2 are used to evaluate the handling stability of the vehicle, j3 is used to evaluate the driving burden, and j4 represents the accuracy of trajectory tracking.

[0078] Add these four evaluation indicators according to the weights to obtain the final energy function:

[0079] E = α1·j1 + α2·j2 + α3·j3 + α4·j4

[0080] Among them, α1, α2, α3, and α4 are weight parameters.

[0081] Given a set of initial values of membership function parameters and fuzzy rule parameters, an initial solution S0 of the simulated annealing algorithm is formed.

[0082] Based on the value of S0, the above variable transmission ratio fuzzy controller is constructed and simulated under the set system model and environment to obtain the initial energy function value E(S0).

[0083] For the outer temperature loop, starting from the initial temperature T0, according to the temperature decay formula:

[0084] T k+1 = α·T k

[0085] The temperature is gradually decreased until the termination temperature T end

[0086] For the inner iteration loop, at each temperature T k , L iterations are performed.

[0087] Each iteration performs the following steps:

[0088] Within the neighborhood of the current solution S k , a new solution S new is randomly generated. The specific process is as follows:

[0089] For continuous variables, that is, the mean and standard deviation of the Gaussian membership function, a new solution is generated by making small random perturbations.

[0090] For discrete variables, that is, fuzzy rule parameters, a value is randomly selected from its discrete value set as the new solution.

[0091] Based on the new solution, the fuzzy controller is reconstructed, and the energy function value E(S new ) of the new solution and the difference ΔE between its energy function value and that of the current solution E(S k ) are calculated:

[0092] ΔE = E(S new ) - E(S k )

[0093] If ΔE < 0, it means the new solution is better, then directly accept the new solution, that is, S k+1 = S new ;

[0094] If ΔE ≥ 0, then accept the new solution with a probability The specific process is as follows:

[0095] Generate a random number r within the interval [0, 1]. If r < P, accept the new solution S k+1 = S new ; Otherwise, keep the current solution S k+1 = S k .

[0096] When the temperature drops to the termination temperature T end , the simulated annealing algorithm ends. The current solution S final at this time is the optimal fuzzy controller parameters searched for.

[0097] According to S final Adjust the membership function parameters and fuzzy rule parameters to obtain an optimized fuzzy controller. Thus, the design of the variable transmission ratio control strategy for the steer-by-wire system based on the parameter-optimized fuzzy controller is completed.

Claims

1. A method for optimizing the variable transmission ratio parameters of a steer-by-wire control system, characterized in that, The steps are as follows: Step 1): Design a variable transmission ratio controller based on the fuzzy control principle. The input variables are vehicle speed and steering wheel angle, and the output variable is the transmission ratio. The Gaussian membership function is used to fuzzify the input and output variables, establish fuzzy rules, and then the centroid method is used for defuzzification to convert the fuzzy output into a specific transmission ratio value; Step 2): Take the parameters of the fuzzy controller as the optimization object, and use the simulated annealing algorithm to optimize the parameters of the fuzzy controller. The initial solution is the initial value of the fuzzy controller parameters; Comprehensively considering the steering response, stability, and driving comfort of the vehicle, design an energy function to evaluate the performance of the fuzzy controller. Through iterative search for the optimal parameters, minimize the energy function to obtain the optimal parameters of the fuzzy controller.

2. The method for optimizing the variable transmission ratio parameter of the steer-by-wire control system according to claim 1, characterized in that The specific content of the said Step 1) includes: The fuzzy controller takes the vehicle speed and steering wheel angle as input variables and the transmission ratio of the steer-by-wire system as the output variable; The fuzzy set of vehicle speed is divided into "low", "medium", and "high", and its domain is set to [0, 120] km / h; the fuzzy set of steering wheel angle is divided into "small", "medium", and "large", and its domain is set to [0, 200] degrees; the fuzzy set of transmission ratio is divided into "small", "medium", and "large", and its domain is set to [5, 30]; The Gaussian membership function is used to fuzzify the exact values of the input and output variables, and its expression is: In the formula, x is the variable value to be fuzzified; c is the average value; σ is the standard deviation; According to the characteristics of different fuzzy sets, set the corresponding Gaussian membership function parameters for each fuzzy set to accurately describe the membership degree distribution of the fuzzy set; Establish fuzzy control rules, use the Mamdani fuzzy inference method, and based on the fuzzy values of the current input vehicle speed and steering wheel angle, combined with the fuzzy control rule base, calculate the fuzzy output set of the transmission ratio; Use the centroid method for defuzzification. According to the fuzzy output set, calculate the final exact control quantity through the calculation formula of the centroid method: where m is the number of fuzzy subsets; a and b are the upper and lower limits of the universe of discourse, respectively; is the membership function corresponding to each fuzzy subset.

3. The variable transmission ratio parameter optimization method for the steer-by-wire control system according to claim 1, characterized in that The specific content of the said Step 2) includes: parameterize the fuzzy controller and use the simulated annealing algorithm to optimize it; Define the membership function parameters as continuous variables and the fuzzy rule parameters as discrete variables; Define the parameters of the simulated annealing algorithm: the initial temperature T0, the temperature decay coefficient α, the number of iterations L at each temperature, and the termination temperature T end ; Comprehensively considering the steering response, stability, and driving comfort of the vehicle, design an energy function: E = α1·j1 + α2·j2 + α3·j3 + α4·j4 where t f is the time of a single test; β is the sideslip angle of the vehicle's center of mass; ω r is the yaw rate of the vehicle; θ sw is the steering wheel angle; e y is the lateral position error when the vehicle performs trajectory tracking, j1 and j2 are used to evaluate the handling stability of the vehicle, j3 is used to evaluate the driving burden, and j4 represents the accuracy of trajectory tracking; α1, α2, α3, α4 are weight parameters; Randomly generate a set of membership function parameters and fuzzy rule parameters to form the initial solution S0; Based on this set of parameters, construct the above variable transmission ratio fuzzy controller and perform simulation under the set system model and environment to obtain the initial energy function value E(S0); For the outer temperature cycle, starting from the initial temperature T0, according to the temperature decay formula: T k+1 = α·T k Gradually reduce the temperature until the termination temperature T is reached end ; For the inner iterative loop, at each temperature T k perform L iterations; Execute the following steps for each iteration: Randomly generate a new solution S k within the neighborhood of the current solution S new Rebuild the fuzzy controller based on the new solution and calculate the energy function value E(S new ) and the difference ΔE between its energy function value E(S k ) and that of the current solution: ΔE = E(S new ) - E(S k ) If ΔE < 0, directly accept the new solution; If ΔE ≥ 0, accept the new solution or keep the current solution with probability P; When the temperature drops to the termination temperature T end , the simulated annealing algorithm ends, and the current solution S final at this time is the optimal fuzzy controller parameters found; According to S final Adjust the membership function parameters and fuzzy rule parameters to obtain an optimized fuzzy controller.

4. The variable steering ratio parameter optimization method for the steer-by-wire control system according to claim 3, characterized in that, If ΔE < 0, then directly accept the new solution, i.e., S k+1 = S new ; If ΔE≥0, then with probability accept the new solution. The specific process is as follows: Generate a random number r in the interval [0, 1]. If r < P, then accept the new solution S k+1 = S new ; Otherwise, keep the current solution S k+1 = S k .

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