A Low-Computational-Complexity Preset Performance Fault-Tolerant Control Method for Steer-by-Wire Systems

Abstract

This invention provides a low-computational-complexity, pre-set performance-tolerant control method for steer-by-wire systems. The control method includes: determining the control objective based on a pre-set performance control method; subsequently designing a new pre-set performance function and error transformation function; designing virtual control laws and control laws in the controller using a robust control method; establishing a dynamic model of the steer-by-wire system based on system dynamics; establishing a dead-zone model and a fault model based on the impact of dead zones and faults in the vehicle actuators on the control performance of the steer-by-wire system; simultaneously obtaining the input nonlinear model of the steer-by-wire system; subsequently establishing a state-space model of the steer-by-wire system; and finally, verifying the controller's control effect and optimizing its internal parameters using the state-space model. This invention considers fault and dead-zone issues, improving the robustness of the controller; and considers the limited computational resources of the vehicle, with a simple controller structure that improves the overall computational efficiency of the vehicle.

Description

Technical Field

[0001] This invention relates to the field of lateral motion actuators for intelligent vehicles—steer-by-wire systems—and particularly to a low-computational-complexity, preset-performance-tolerant control method for steer-by-wire systems. Background Technology

[0002] With the deep integration of automation, intelligence, and the automotive industry, autonomous vehicles have received widespread attention and research from academia and industry due to their superior performance in traffic safety and passenger comfort. Among them, the Steer-by-Wire (SbW) system, as one of the execution systems of autonomous vehicles, adjusts the output torque of the steering motor to make the steering angle of the front wheels track the desired signal, thereby ensuring that the autonomous vehicle travels along the path planned by the upper layer.

[0003] Currently, in the field of online steering control systems, researchers have proposed sliding mode control methods for SbW systems based on parameter identification technology, or model predictive control methods for SbW systems with actuator failures, model uncertainties, and external disturbances based on prior bounds for model uncertainties and disturbances. Furthermore, to ensure the transient and steady-state performance of SbW systems, a performance-defined controller based on a type II fuzzy logic system has been developed. Considering the bandwidth limitations of the controller area network in controller-actuator communication, an event-triggered controller for SbW systems based on a fuzzy logic system has been proposed. In the area of ​​nonlinear control systems, for problems with full-state constraints, back-reasoning techniques are introduced, combining nonlinear variations, barrier Lyapunov functions (BLFs), and exponentially decaying nonlinear mappings to construct tracking controllers. For nonlinear systems with input saturation, researchers have considered back-reasoning techniques in controller design, combining dynamic gain methods, auxiliary system methods, and function approximation methods based on the mean value theorem or Gaussian error functions. The controller designs described above are complex and require a large amount of computing resources to operate. Therefore, designing a low-complexity controller while ensuring the tracking performance of the control system is an urgent problem to be solved.

[0004] Existing steer-by-wire systems employ pre-defined tracking control design techniques that utilize radial basis function neural networks to approximate the uncertainties of nonlinear systems online, combined with obstacle Lyapunov function techniques for controller design. However, these methods firstly fail to consider the faults and dead zones of the steer-by-wire system, and secondly, the use of radial basis function neural networks for online approximation increases the computational load. Therefore, the designed controllers cannot guarantee good transient and steady-state tracking performance in actual steer-by-wire systems under low torque and rapid changes in front wheel steering angle. Summary of the Invention

[0005] In view of this, the present invention provides a low computational complexity preset performance fault-tolerant control method for a steer-by-wire system to solve the above problems.

[0006] According to a first aspect of the present invention, a low-computational-complexity preset performance fault-tolerant control method for a steer-by-wire system is provided, comprising: determining a control objective based on a preset performance control method; designing a new preset performance function and an error transformation function according to the control objective; designing a virtual control law and a control law in a controller by combining a robust control method; establishing a dynamic model of the steer-by-wire system based on system dynamics; establishing a dead-zone model and a fault model based on the influence of dead-zone and faults of the vehicle actuators on the control performance of the steer-by-wire system; obtaining an input nonlinear model of the steer-by-wire system based on the dead-zone model and the fault model; establishing a state-space model of the steer-by-wire system based on the input nonlinear model and the dynamic model of the steer-by-wire system; and verifying the control effect of the controller and optimizing its internal parameters based on the state-space model.

[0007] In another implementation of the present invention, the control objective is defined as:

[0008] -k≤yy d ≤k, t>t k

[0009] Among them, y d The desired signal is the desired front wheel steering angle, t. k It is the time point at which the specified error is reached, k is the specified tracking error range, and y d Both their derivatives with respect to time are continuous, bounded, and differentiable.

[0010] In another implementation of the present invention, the new preset performance function and error transformation function include:

[0011] The preset performance function p1(t) is designed as follows:

[0012]

[0013] Where k and ξ are the positive constants to be designed;

[0014] The error transformation function is designed as follows:

[0015] z1 = x1 - y d

[0016] z² = x² - α(t)

[0017] Here, x1 and x2 are both state variables of the control, and α(t) is the virtual control law.

[0018] In another implementation of the present invention, the virtual control law and the control law in the controller are designed as follows:

[0019] The virtual control law α(t) is designed as follows:

[0020]

[0021] Where η1 is the positive constant to be designed and optimized;

[0022] The control law u(t) is designed as follows:

[0023]

[0024] Here, η2 and p2 are positive constants that need to be designed and optimized.

[0025] In another implementation of the present invention, the low computational complexity steer-by-wire system preset performance fault-tolerant control method further includes:

[0026] The dynamic model of the steer-by-wire system is expressed as follows:

[0027]

[0028] Where, θ f B represents the steering angle of the front wheels, μ represents the ratio of the motor output shaft rotation angle to the front wheel rotation angle. m τ represents the viscous friction of the steer-by-wire system. m J is the output torque of the steering motor. e and H f These are the equivalent rotational inertia and the uncertain nonlinear quantity of the steer-by-wire system, respectively.

[0029] In another implementation of the present invention, the equivalent moment of inertia and the uncertain nonlinear quantity can be calculated using the following formula:

[0030] J e =J f +μ 2 J m

[0031] H f =τ e +τ f

[0032] Among them, J f and J m The moments of inertia τ of the front wheels and the steering motor are respectively. e and τ f These are the self-aligning torque and friction torque of the front wheels, respectively. The specific calculation formulas are as follows:

[0033]

[0034]

[0035] Among them, V CG For a constant longitudinal speed of the vehicle, C f t is the front tire lateral stiffness coefficient. p and t m These are aerodynamic and mechanical trajectories, l f Let β be the distance from the front axle to the center of gravity, and γ be the body slip angle and yaw rate at the center of gravity (CG), respectively. The calculation formula is as follows:

[0036]

[0037] Among them, C r The rear tire lateral stiffness coefficient is given by l, where m is the vehicle mass and l is the vehicle weight. r and I z These are the distance from the rear axle to the center of gravity and the vehicle's moment of inertia about the center of gravity, respectively.

[0038] In another implementation of the present invention, the dead-time model and the fault model are respectively represented as:

[0039]

[0040] Among them, u d and v d (u d ) represent the input and output of the dead zone, respectively; in addition, there exists a positive constant σ. imin , σ imax and Make σ imin ≤σ i (t)≤σ imax ,

[0041]

[0042] Among them, u f and v f (u f ) represent the input and output of the fault model, respectively, σ f (t) represents the failure factor of the actuator, and there exists a positive constant σ. fmin , so that σ fmin <σ f (t)≤1, The bias signal is time-varying and has normal values. and Make

[0043] In another implementation of the present invention, based on the dead zone model and the fault model, the relationship between the input and output of the input nonlinear model of the steer-by-wire system is determined as follows:

[0044]

[0045] Where u and v(u) are the control law and the output of the input nonlinear model, respectively;

[0046] σ(t) and They are respectively:

[0047]

[0048]

[0049] Where, σ f (t) represents the failure factor of the actuator, and there exists a positive constant σ. fmin σ imin and σ imax , so that σ fmin <σ f (t)≤1、σ imin ≤σ i (t)≤σ imax (i = r, l), The bias signal is time-varying and has normal values. and Make

[0050] In another implementation of the present invention, the state-space model of the steer-by-wire system is represented as follows:

[0051]

[0052] y = x1

[0053] in, Both are state vectors, where y∈R represents the output of the steer-by-wire system, u is the designed control law, and f(x,t):R 2 ×R + →R represents the total uncertain nonlinearity, including frictional torque and restoring torque, and g(t)∈R are the uncertain control coefficients, which can be expressed as follows:

[0054]

[0055] Where μ is the ratio of the motor output shaft rotation angle to the front wheel rotation angle, and B m J is the coefficient of viscous friction of the steer-by-wire system. f and J m These are the moments of inertia of the front wheels and the steering motor, respectively.

[0056] In another implementation of the present invention, the controller's control effect is verified and its internal design parameters are optimized based on the state-space model, including: the controller verifies the control effect and optimizes the design parameters within the controller by controlling the state-space model of the steering-by-wire system.

[0057] In the low computational complexity steer-by-wire system preset performance fault-tolerant control method of the present invention, considering the computational complexity of the control system and the input nonlinearity and parameter uncertainty of the steer-by-wire system, i.e., fault problems and dead zone phenomena, incorporating faults and dead zones as uncertain factors can improve the robustness of the designed controller. Considering the limited computing resources of the whole vehicle, a controller with a simple structure is designed by combining robust control methods and preset performance control methods without affecting robustness, thereby improving the computing efficiency of the whole vehicle. By designing a preset tracking error as the control target and an adjustable performance function, the transient and steady-state performance of the steer-by-wire system is improved, reducing the possibility of dangerous situations during the operation of autonomous vehicles and improving ride comfort. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. By reading the detailed description of the embodiments below, the advantages and benefits of the solutions will become clear to those skilled in the art. The accompanying drawings are only for illustrating preferred embodiments and are not intended to limit the present invention. In the accompanying drawings:

[0059] Figure 1 This is a flowchart illustrating the steps of a low-computational-complexity steer-by-wire system preset performance fault-tolerant control method according to an embodiment of the present invention.

[0060] Figure 2 This is a simplified schematic diagram of a preset performance control method according to another embodiment of the present invention.

[0061] Figure 3 This is a block diagram of the controller structure in a low computational complexity steer-by-wire system preset performance fault-tolerant control method according to another embodiment of the present invention.

[0062] Figure 4 This is a simplified schematic diagram of a steer-by-wire system model according to another embodiment of the present invention.

[0063] Figure 5 This is a flowchart illustrating the process of verifying and optimizing parameters for the controller, according to another embodiment of the present invention. Detailed Implementation

[0064] To enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and thoroughly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art should fall within the protection scope of the present invention.

[0065] Figure 1 A flowchart illustrating the steps of a low-computational-complexity steer-by-wire system preset performance fault-tolerant control method provided in this embodiment of the invention is shown below. Figure 1 As shown, this embodiment mainly includes the following steps:

[0066] S101. Determine the control objective based on the preset performance control method.

[0067] For example, Figure 2 A simplified diagram illustrating the preset performance control method, such as... Figure 2 As shown, the transient and steady-state performance of a system is one of the indicators for evaluating the quality of a controller. Therefore, the following control objective is determined, which can make the system error converge to the steady state within a specified time (i.e., transient performance) and ensure that the system error remains stable within a specified range as time approaches infinity (i.e., steady-state performance).

[0068] S102. Based on the control objective, design new preset performance functions and error transformation functions.

[0069] For example, Figure 2 A simplified diagram illustrating the preset performance control method, such as... Figure 2 As shown, the preset performance control is based on the performance function and error transformation to construct an equivalent model, and then the controller is designed based on this equivalent model. Therefore, new preset performance functions and error transformation functions are introduced.

[0070] S103. Based on robust control methods and preset performance functions, design virtual control laws and control laws.

[0071] For example, Figure 3 The block diagram of the controller is as follows: Figure 3 As shown, the control objective, preset performance function, and error transformation function are determined. In order to meet the transient and steady-state performance of the control, the virtual control law and the control law are obtained through back-reasoning techniques.

[0072] S104. Based on system dynamics, establish a dynamic model of the steer-by-wire system.

[0073] For example, Figure 4 A simplified schematic diagram of a steer-by-wire system model, such as... Figure 4 As shown, the steer-by-wire system includes a steering motor, reducer, controller, front wheels, gearbox, and other structures. Its working principle is simply as follows: The planning and decision-making layer of the autonomous vehicle calculates the desired front wheel steering angle and converts it into a digital signal, which is then transmitted to the SbW controller. The SbW controller receives the signal and collects the actual front wheel steering angle signal. The controller calculates the control quantity of the steering motor based on the desired front wheel steering angle and the actual steering angle signal. The steering motor receives the controller's command and outputs torque, which finally drives the steering tie rod to change the steering angle of the front wheels through the reducer and rack and pinion steering mechanism.

[0074] S105. Based on the impact of dead zone and faults of automotive actuators on the control performance of steer-by-wire system, establish dead zone model and fault model.

[0075] For example, during the execution of the target torque by the actuator, there is a torque zero-crossing region, i.e., a dead zone. To describe the motor output characteristics within the zero-crossing region, a dead zone model is established. Furthermore, the steering motor of the SbW system is highly sensitive to electromagnetic interference and electronic faults. Therefore, the impact of actuator faults on the control performance of the SbW system is considered, and actuator faults in the SbW system are modeled.

[0076] S106. Based on the dead zone model and the fault model, the input nonlinear model of the steer-by-wire system is obtained.

[0077] For example, by combining steps S103 and S104, the relationship between the input and output of the input nonlinear model of the steer-by-wire system is determined, and the input nonlinear model of the steer-by-wire system is obtained based on the relationship between the model input and output.

[0078] S107. Establish a state-space model of the steer-by-wire system based on the dynamic model and input nonlinear model of the steer-by-wire system.

[0079] For example, the state-space model of the steer-by-wire system is only used to verify and optimize the controller design parameters. The state-space model conforms to the theoretical description of the control system in form and also conforms to the actual application situation in content.

[0080] S108. Based on the state-space model, verify the control effect of the controller and optimize its internal parameters.

[0081] For example, Figure 5 A flowchart for the process of validating and optimizing the controller parameters, such as... Figure 5 As shown, the control effect of the controller can be graphically displayed through the state-space model and related software, and the control effect can be optimized by adjusting the internal parameters of the controller.

[0082] It should be understood that, as Figure 4As shown, in practical applications, the control quantity output by the designed controller is the output steering torque of the steering motor. Under the control of the controller, the torque of the steering motor drives the steering tie rod through the reducer and the rack and pinion steering mechanism to change the steering angle of the front wheels.

[0083] In the low computational complexity steer-by-wire system preset performance fault-tolerant control method of the present invention, considering the computational complexity of the control system and the nonlinearity of the model input and parameter uncertainty of the steer-by-wire system, i.e., fault problems and dead zone phenomena, incorporating faults and dead zones as uncertain factors can improve the robustness of the designed controller. Considering the limited computing resources of the whole vehicle, a controller with a simple structure is designed by combining robust control methods and preset performance control methods without affecting robustness, thereby improving the computational efficiency of the whole vehicle. By designing a preset tracking error as the control target and an adjustable performance function, the transient and steady-state performance of the steer-by-wire system is improved, reducing the possibility of dangerous situations during the operation of autonomous vehicles and improving ride comfort.

[0084] In another implementation of the present invention, the control objective is determined as follows:

[0085] -k≤yy d ≤k, t>t k

[0086] Among them, y d The desired signal is the desired front wheel steering angle, t. k It is the time point at which the specified error is reached, k is the specified tracking error range, and y d Both their derivatives with respect to time are continuous, bounded, and differentiable.

[0087] In another implementation of the present invention, the new preset performance function and error transformation function include:

[0088] The preset performance function p1(t) is designed as follows:

[0089]

[0090] Here, k and ξ are the positive constants to be designed.

[0091] The error transformation function is designed as follows:

[0092] z1 = x1 - y d

[0093] z² = x² - α(t)

[0094] Here, x1 and x2 are both state variables of the control, and α(t) is the virtual control law.

[0095] In another implementation of the present invention, the virtual control law and the control law in the controller are designed as follows:

[0096] The virtual control law α(t) is designed as follows:

[0097]

[0098] Here, η1 is the positive constant to be designed and optimized.

[0099] The control law u(t) is designed as follows:

[0100]

[0101] Here, η2 and p2 are positive constants that need to be designed and optimized.

[0102] In another implementation of the present invention, the dynamic model of the steer-by-wire system is expressed as follows:

[0103]

[0104] Where, θ f The steering angle of the front wheels can be calculated in real time using signals from the front wheel steering angle sensor. μ represents the ratio of the motor output shaft rotation angle to the front wheel rotation angle. m μ and B represent the viscous friction of the steer-by-wire system. m It can be preset based on experience and actual conditions, τ m J is the output torque of the steering motor. e and H f These are the equivalent rotational inertia and the uncertain nonlinear quantity of the steer-by-wire system, respectively.

[0105] In another implementation of the present invention, the equivalent moment of inertia and the uncertain nonlinear quantity can be calculated using the following formula:

[0106] J e =J f +μ 2 J m

[0107] H f =τ e +τ f

[0108] Among them, J f and J m These are the moments of inertia of the front wheels and the steering motor, respectively, which can be collected and stored in advance based on actual vehicle design parameters or test data. e and τ f These are the self-aligning torque and friction torque of the front wheels, respectively. The specific calculation formulas are as follows:

[0109]

[0110] Among them, V CG The constant longitudinal speed of the vehicle can be obtained in real time by measuring the wheel speed sensor signal. C f t is the front tire lateral stiffness coefficient. p and t m These are aerodynamic and mechanical trajectories, l f C is the distance from the front axle to the center of gravity. f and l f Data can be collected and stored in advance based on actual vehicle design parameters or test data. p and t m It is not easy to measure accurately, but it can be preset based on experience and actual conditions.

[0111] β and γ are the body slip angle and yaw rate at the center of gravity (CG), respectively, and are calculated using the following formulas:

[0112]

[0113] Among them, C r The rear tire lateral stiffness coefficient is given by l, where m is the vehicle mass and l is the vehicle weight. r and I z These are the distance from the rear axle to the center of gravity and the vehicle's moment of inertia about the center of gravity, respectively. These parameters can be collected and stored in advance based on actual vehicle design parameters or test data.

[0114] In another implementation of the present invention, the dead-time model and the fault model are respectively represented as:

[0115]

[0116] Among them, u d and v d (u d ) represent the input and output of the dead zone, respectively; in addition, there exists a positive constant σ. imin , σ imax and Make σ imin ≤σ i (t)≤σ imax ,

[0117]

[0118] Among them, u f and v f (u f ) represent the input and output of the fault model, respectively, σ f (t) represents the failure factor of the actuator, and there exists a positive constant σ. fmin , so that σ fmin<σ f (t)≤1, The bias signal is time-varying and has normal values. and

[0119] In another implementation of the present invention, based on the dead zone model and the fault model, the relationship between the input and output of the input nonlinear model of the steer-by-wire system is determined as follows:

[0120]

[0121] Where u and v(u) are the control law of the design and the output of the input nonlinear model, respectively.

[0122] σ(t) and They are respectively:

[0123]

[0124] Where, σ f (t) represents the failure factor of the actuator, and there exists a positive constant σ. fmin σ imin and σ imax , so that σ fmin <σ f (t)≤1、σ imin ≤σ i (t)≤σ imax (i = r, l), The bias signal is time-varying and has normal values. and Make

[0125] Based on the input nonlinear model and the dynamic model of the steer-by-wire system, the state-space model of the steer-by-wire system is obtained.

[0126] In another implementation of the present invention, it further includes:

[0127] The state-space model of the steer-by-wire system is represented as follows:

[0128]

[0129] y = x1

[0130] in, Both are state vectors, where y∈R represents the output of the steer-by-wire system, u is the designed control signal, and f(x,t):R 2 ×R + →R represents the total uncertain nonlinearity, including frictional torque and restoring torque, and g(t)∈R are the uncertain control coefficients, which can be expressed as follows:

[0131]

[0132] Where μ is the ratio of the motor output shaft rotation angle to the front wheel rotation angle, and B m J is the coefficient of viscous friction of the steer-by-wire system. f and J m These are the moments of inertia of the front wheels and the steering motor, respectively.

[0133] In another implementation of the present invention, based on the state-space model, verifying the control effect of the controller and optimizing its internal design parameters includes: the controller verifies the control effect and optimizes the design parameters within the controller by controlling the state-space model of the steering-by-wire system.

[0134] This invention uses a preset performance control method to design the controller, where the specified convergence time t is specified in this method. k The specified convergence range k is adjustable, which ensures the transient and steady-state performance of the controller and expands the application scenarios of the controller in practical applications. Moreover, the controller has a simple structure and does not use parameter estimators or online approximation methods, which effectively reduces the computational complexity of the vehicle processing unit, improves processing efficiency, and enhances the safety and ride comfort of autonomous vehicles.

[0135] Specific embodiments of the invention have now been described. Other embodiments are within the scope of the appended claims. In some cases, the actions described in the claims can be performed in a different order and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing can be advantageous.

[0136] It should be noted that all directional indicators (such as up, down, left, right, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicator will also change accordingly.

[0137] In the description of this invention, the terms "first" and "second" are used only for convenience in describing different components or names, and should not be construed as indicating or implying a sequential relationship, relative importance, or implicitly specifying the number of technical features indicated. Thus, a feature defined with "first" and "second" may explicitly or implicitly include at least one of that feature.

[0138] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0139] It should be noted that although specific embodiments of the present invention have been described in detail with reference to the accompanying drawings, this should not be construed as limiting the scope of protection of the present invention. Various modifications and variations that can be made by those skilled in the art without inventive effort within the scope described in the claims still fall within the scope of protection of the present invention.

[0140] The examples of the embodiments of the present invention are intended to concisely illustrate the technical features of the embodiments of the present invention, so that those skilled in the art can intuitively understand the technical features of the embodiments of the present invention, and are not intended to be an improper limitation of the embodiments of the present invention.

[0141] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A low-computational-complexity, pre-set performance fault-tolerant control method for a steer-by-wire system, characterized in that, include: Based on a preset performance control method, a control objective is determined, wherein the control objective is: -k≤y-y d ≤k,t>t k Among them, y d The desired signal is the desired front wheel steering angle, t. k It is the time point at which the specified error is reached, k is the designed specified tracking error range, and y d Both their time derivatives are continuous, bounded, and differentiable; Based on the control objective, a new preset performance function and an error transformation function are designed, wherein the new preset performance function and error transformation function include: The preset performance function p1(t) is designed as follows: Where k and ξ are the positive constants to be designed; The error transformation function is designed as follows: z1=x1-y d z² = x² - α(t) Where x1 and x2 are both control state variables, and α(t) is the virtual control law; By combining robust control methods, design the virtual control law and control law in the controller; Based on system dynamics, a dynamic model of the steer-by-wire system is established; Based on the impact of dead zone and faults of automotive actuators on the control performance of steer-by-wire systems, dead zone and fault models are established, which can be expressed as follows: Among them, u d and v d (u d ) represent the input and output of the dead zone, respectively. Additionally, there exists a positive constant σ. imin , σ imax and Make σ imin ≤σ i (t)≤σ imax , Among them, u f and v f (u f ) represent the input and output of the fault model, respectively, σ f (t) represents the failure factor of the actuator, and there exists a positive constant σ. fmin , so that σ fmin <σ f (t)≤1, The bias signal is time-varying and has normal values. and Make Based on the dead zone model and the fault model, the input nonlinear model of the steer-by-wire system is obtained; A state-space model of the steer-by-wire system is established based on the dynamic model of the steer-by-wire system and the input nonlinear model. Based on the state-space model, the controller's control effect is verified and its internal parameters are optimized.

2. The method according to claim 1, characterized in that, The virtual control law and control law include: The virtual control law is designed as follows: Where η1 is the positive constant to be designed and optimized; The control law u(t) is designed as follows: Here, η2 and p2 are positive constants that need to be designed and optimized.

3. The method according to claim 1, characterized in that, The dynamic model of the steer-by-wire system is expressed as follows: Where, θ f B represents the steering angle of the front wheels, μ represents the ratio of the motor output shaft rotation angle to the front wheel rotation angle. m τ represents the viscous friction of the steer-by-wire system. m J is the output torque of the steering motor. e and H f These are the equivalent rotational inertia and the uncertain nonlinear quantity of the steer-by-wire system, respectively.

4. The method according to claim 3, characterized in that The equivalent moment of inertia and the uncertain nonlinearity can be calculated using the following formula: J e *J f +μ 2 J m H f =t e +t f Among them, J f and J m The moments of inertia τ of the front wheels and the steering motor are respectively. e and τ f These are the self-aligning torque and friction torque of the front wheels, respectively. The specific calculation formulas are as follows: Among them, V CG For a constant longitudinal speed of the vehicle, C f Let t be the lateral stiffness coefficient of the front wheel tire. p and t m These are aerodynamic and mechanical trajectories, l f Let β be the distance from the front axle to the center of gravity, and γ be the body slip angle and yaw rate at the center of gravity (CG), respectively. The calculation formula is as follows: Among them, C r The rear tire lateral stiffness coefficient is given by l, where m is the vehicle mass and l is the vehicle weight. r and I z These are the distance from the rear axle to the center of gravity and the vehicle's moment of inertia about the center of gravity, respectively.

5. The method according to claim 1, characterized in that, Based on the dead zone model and the fault model, the relationship between the input and output of the input nonlinear model of the steer-by-wire system is obtained as follows: Where u and v(u) are the control law and the output of the input nonlinear model, respectively; The σ(t) and They are respectively: Where, σ f (t) represents the failure factor of the actuator, and there exists a positive constant σ. fmin σ imin and σ imax , so that σ fmin <σ f (t)≤1、σ imin ≤σ i (t)≤σ imax (i = r, l), The bias signal is time-varying and has normal values. and Make 6. The method according to claim 5, characterized in that, The state-space model of the steer-by-wire system can be represented as follows: y = x1 in, Let y ∈ R be the state vector, representing the output of the steer-by-wire system, u be the designed control law, and f(x,t): R 2 ×R + →R represents the total uncertain nonlinearity, including frictional torque and restoring torque, and g(t)∈R are the uncertain control coefficients, which can be expressed as follows: Where μ is the ratio of the motor output shaft rotation angle to the front wheel rotation angle, and B m J is the coefficient of viscous friction of the steer-by-wire system. f and J m These are the moments of inertia of the front wheels and the steering motor, respectively.

7. The method according to claim 1, characterized in that, Based on the state-space model, the controller's control effect is verified and its internal parameters are optimized, including: The controller verifies the control effect and optimizes the design parameters within the controller by controlling the state space model of the steer-by-wire system.

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