A centralized control method of steer-by-wire system based on non-singular state transformation

Abstract

The application discloses a centralized control method of a steer-by-wire system based on a non-singular state transformation, relates to the field of steer-by-wire system control, and comprises the following steps: establishing an SBW system centralized control model integrated with lateral stability, steering angle tracking accuracy and motor dynamic characteristics, solving the problem of limited bandwidth and restricted response speed caused by a traditional hierarchical control architecture; designing a non-singular state transformation matrix, decomposing the SBW system centralized control model into five independent subsystems, realizing model dimension reduction and decoupling, and reducing the complexity of controller design; and on the basis, proposing an anti-saturation integral terminal sliding mode control method and a parameter design method meeting the hierarchical convergence of system states. The application realizes accurate, smooth and robust tracking of the given steering angle of a driver by improving the dynamic response quality of a steering motor under the premise of ensuring the lateral stability of a vehicle.

Description

Technical Field

[0001] This invention relates to the field of steer-by-wire system control, and mainly to a centralized control method for steer-by-wire systems based on non-singular state transformation. Background Technology

[0002] With the continuous advancement of automotive electrification and intelligence, steer-by-wire (SBW) systems eliminate the mechanical connection between the steering wheel and the steering actuator. They offer advantages such as fast response, flexible spatial layout, and ease of integration with advanced driver assistance systems, making them a key enabling technology for intelligent connected vehicles and a core component of steer-by-wire chassis systems. This provides a more adaptable and efficient solution for modern vehicle architectures. However, SBW systems are complex nonlinear systems, facing multiple uncertainties in actual operation, including parameter perturbations, external disturbances, and unmodeled dynamics. The performance of the steer-by-wire system controller directly determines the vehicle's handling stability and safety. Domestic and international experts and scholars have conducted extensive and in-depth research on the control architecture and algorithms of steer-by-wire systems. Commonly used steer-by-wire and steering motor control methods include PID control, robust control, intelligent control, model predictive control, sliding mode control, and others.

[0003] To further improve the performance of SBW systems, many studies have combined the yaw stability of AFS with front wheel angle tracking based on the methods in the aforementioned literature, and adopted a hierarchical control architecture to further improve system performance. The literature [Zhang J,Wang H, Ma M, et al. Active Front Steering-Based Electronic Stability Control for Steer-by-Wire Vehicles via Terminal Sliding Mode and Extreme Learning Machine[J]. IEEE Transactions on Vehicular Technology, 2020, 69(12): 14713-14726.] uses adaptive recursive integral terminal sliding mode control for the upper controller, which has strong robustness and convergence speed. The lower controller combines fast non-singular terminal sliding mode control and extreme learning machine estimator to achieve tracking control of the desired steering angle. The literature [Zhang J, Wang H, Zheng JC, et al. Adaptive Sliding Mode-Based Lateral Stability Control of Steer-by-Wire Vehicles With Experimental Validations[J]. IEEE Transactions on Vehicular Technology, 2020, 69(9): 9589-9600.] achieves precise control of rotation angle by combining an adaptive sliding mode method with a hierarchical architecture.The literature [Wang JZ, Zhao YQ, Lin F, Liu YB. Active front steering-based lateral stability control for steer-by-wire vehicles with uncertainties via robust optimal control and terminal sliding mode[J / OL]. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 2026-01-17.https: / / doi.org / 10.1177 / 09544070251406926.] designs an upper-level AFS controller based on adaptive robust optimization using LQR optimal control, SMC, and adaptive control techniques. This controller not only optimizes the control objective but also possesses a certain degree of robustness, ensuring the convergence of the vehicle's actual center of gravity sideslip angle and yaw rate. The lower-level adaptive synchronous non-singular fast terminal sliding mode controller, based on a neural network approximator, is designed to track the desired front wheel steering angle calculated by the upper-level controller, providing high robustness, fast transient response, and finite-time convergence for the entire SBW system.

[0004] Currently, most SBW control research adopts a hierarchical control architecture, such as the literature [Zhang J, Wang H, Zheng JC, et al. Adaptive Sliding Mode-Based Lateral Stability Control of Steer-by-Wire Vehicles With Experimental Validations[J]. IEEE Transactions on Vehicular Technology, 2020, 69(9): 9589-9600.]. Based on the driver's steering intention, the front wheel angle and the current vehicle speed are input to the reference model to obtain the ideal yaw rate. A stability controller is designed to make the state variables track the ideal value and output compensation for the front wheel angle. The lower-level angle tracking controller controls the steering actuator to output torque to track the upper-level compensation angle.

[0005] The centralized control architecture has the highest integration. According to the control requirements, it uses a variety of sensors and observers to collect vehicle information to the central controller, thereby realizing information sharing and avoiding hardware redundancy. Then, the controller sends the execution instructions directly to the actuators of each subsystem through a global coordination strategy or optimization algorithm. The development difficulty is also the greatest. The literature [Cai YF, Li YX, Lian YB, et al. Cooperative Control of Coupled Multiagent System of Autonomous Vehicle Chassis Based on Co-DMPC[J]. IEEE Transactions on Transportation Electrification, 2025, 11(1): 1875-1890.] proposes a multi-agent cooperative control architecture based on cooperative distributed model predictive control to address the control challenges of four-wheel drive / four-wheel steering vehicles caused by complex dynamic coupling, multiple control inputs and computational burden. It clarifies the coupling mechanism between agents by establishing distributed state equations and reduces the control input of each controller. The literature [Wang H, Kong HF, Man ZH, et al. Sliding Mode Control for Steer-by-Wire Systems With AC Motors in Road Vehicles[J]. IEEE Transactions on Industrial Electronics, 2014, 61(3): 1596-1611.] conducted an in-depth study on the modeling of SBW systems, and established an equivalent second-order system model from the steering motor to the controlled front wheel. Based on this model, a sliding mode controller was designed based on the bounded information of uncertain system parameters, uncertain restoring torque and uncertain torque pulsation disturbance, which improved the robustness of the system.

[0006] Analysis shows that the cascaded hierarchical control architecture from the whole vehicle to the steering actuator limits the bandwidth of the SBW system, thus restricting the response speed; while the SBW system with a centralized control architecture is prone to the "curse of dimensionality" and computational complexity, which increases the design difficulty of the controller.

[0007] Therefore, considering the computational complexity of steer-by-wire systems with centralized control architectures, the bandwidth limitations restricting the system response speed of steer-by-wire systems with hierarchical control architectures, and the existence of complex external disturbances and unmodeled dynamic problems, this invention proposes a centralized control method for steer-by-wire systems based on non-singular state transformations. This method comprehensively considers lateral stability, steering angle tracking accuracy, and motor dynamic characteristics to establish a centralized control model for the SBW system. Through non-singular state transformations, it achieves dimensionality reduction and decoupling of the model. Based on this, it proposes an anti-saturation integral terminal sliding mode control strategy and parameter designs that satisfy the hierarchical convergence of system states, thereby improving the safety and response quality of the SBW system while ensuring vehicle handling. Summary of the Invention

[0008] To address the computational complexity of the centralized control architecture and the bandwidth limitations of the hierarchical control architecture that restrict system response speed in SBW systems, this invention proposes a centralized control method for steer-by-wire systems based on non-singular state transformation. While ensuring the lateral stability of the vehicle, this method improves the dynamic response quality of the steering motor, enabling precise, smooth, and robust tracking of the front wheel steering angle to the driver's given steering angle.

[0009] The technical solution adopted by this invention to solve the technical problem is as follows:

[0010] A centralized control method for a steer-by-wire system based on non-singular state transformation, comprising the following steps:

[0011] Step 1: First, use the CarSim car model to obtain the vehicle's real-time sideslip angle. yaw rate Longitudinal speed The rotational angular velocity of the steering motor is obtained through the steer-by-wire system. Actual front wheel steering angle Three-phase current of the motor , , State variables; through Clark and Park transformations, the three-phase AC current... , , Convert to shaft current and shaft current Then, based on the vehicle's dynamics mechanism, an integrated two-degree-of-freedom dynamic model of the vehicle and a dynamic model of the steer-by-wire actuator are established. A centralized control model of the SBW system based on the PMSM voltage equations in the coordinate system; finally, based on the given longitudinal vehicle speed... and ideal front wheel steering angle Calculate the ideal yaw rate Ideal centroid side slip angle Ideal shaft current and voltage and ideal shaft voltage ;

[0012] Step 2: Design the non-singular state transformation matrix Non-singular state transformation is performed on the established centralized control model of the SBW system to achieve dimensionality reduction and decoupling of the model. The subsystems after dimensionality reduction and decoupling are divided into controllable subsystems and internal subsystems.

[0013] Step 3: First, based on the centroid sideslip angle obtained in Step 1... yaw rate Longitudinal speed ; Rotational angular velocity of the steering actuator motor Actual front wheel steering angle , shaft current , shaft current Ideal front wheel steering angle Ideal yaw rate Ideal centroid side slip angle Ideal shaft current , shaft voltage Ideal shaft voltage and the given longitudinal speed The control voltage required by the steering actuator motor is obtained through the interference observer module and the integral terminal sliding mode control module of the energy control subsystem. and the state variables of the controllable subsystem Then, through the parameter design module of the internal subsystem, reasonable design parameters are selected according to the system performance requirements while ensuring the controllability of the internal subsystem. Combined with the state variables of the energy control subsystem This allows the state variables of the internal subsystems to gradually converge to zero in a hierarchical order; finally, the control voltage required for the steering actuator motor is... After inverse Park converter, SVPWM module and inverter, it is converted into the three-phase AC voltage required by the steering actuator motor. , , It is then transmitted to the steer-by-wire system, which generates the actual front wheel steering angle. And then it is delivered to CarSim car models.

[0014] The beneficial effects of this invention are as follows:

[0015] (1) Based on the dynamic mechanism of vehicles, this invention establishes an integrated two-degree-of-freedom dynamic model of the vehicle and a dynamic model of the steer-by-wire actuator. The centralized control model of the SBW system based on the PMSM voltage equation in the coordinate system solves the problem of limited bandwidth and restricted response speed caused by the traditional cascaded hierarchical control architecture from the whole vehicle to the steering actuator motor.

[0016] (2) The present invention designs a non-singular state transformation matrix to simplify the complex, strongly coupled, and high-dimensional centralized SBW control model into a low-dimensional, decoupled block energy control standard form, thereby achieving dimensionality reduction and decoupling of the model and reducing the complexity of controller design.

[0017] (3) This invention proposes a hierarchical sliding mode control method for SBW systems based on integral terminal sliding mode, and selects reasonable design parameters according to system performance requirements on the premise of ensuring the controllability of internal subsystems. This allows the state variables of the SBW system to gradually converge to zero in a hierarchical order, thereby improving the robustness and steering safety of the entire SBW system.

[0018] (4) The method of the present invention is simple and easy to implement, has a wide range of applications, and is suitable for widespread promotion and application. Attached Figure Description

[0019] Figure 1 This is a block diagram illustrating the principle of a centralized control method for a steer-by-wire system based on non-singular state transformation, as described in this invention. Detailed Implementation

[0020] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0021] like Figure 1 As shown, the centralized control method for a steer-by-wire system based on non-singular state transformation according to the present invention includes: First, using the steer-by-wire actuator system module and the CarSim vehicle model, obtaining the state variables required by the hierarchical sliding mode control module of the SBW system; based on the vehicle dynamics mechanism, establishing an integrated two-degree-of-freedom dynamic model of the vehicle, a dynamic model of the steer-by-wire actuator, and... A centralized control model of the SBW system based on the PMSM voltage equation in the coordinate system, and according to the given longitudinal vehicle speed. and ideal front wheel steering angle Calculate the ideal state variables and ideal values ​​of the centralized control model of the SBW system. shaft current and voltage Ideal shaft voltage Secondly, design the non-singular state transformation matrix. The centralized control model of the SBW system undergoes a non-singular state transformation, and the reduced and decoupled subsystems are divided into controllable subsystems and internal subsystems. Furthermore, the control voltage required for the steering actuator motor is obtained through the SBW system's hierarchical sliding mode control module. The SBW system's hierarchical sliding mode control module includes: a disturbance observer module designed for the energy control subsystem and an integral terminal sliding mode control module, used to obtain the control voltage required by the steering actuator motor. and the state variables of the controllable subsystem The parameter design module for internal subsystems selects appropriate design parameters based on system performance requirements while ensuring the controllability of the internal subsystems. Combined with the state variables of the energy control subsystem This process involves gradually converging the state variables of the internal subsystems to zero in a hierarchical order; finally, the control voltage required by the steering actuator is obtained. After inverse Park converter, SVPWM module and inverter, it is converted into the three-phase AC voltage required by the steering actuator motor. , , It is then transmitted to the steer-by-wire system, which generates the actual front wheel steering angle. And then it is delivered to CarSim car models.

[0022] This invention discloses a centralized control method for a steer-by-wire system based on non-singular state transformation, the specific implementation steps of which are as follows:

[0023] Step 1: First, use the CarSim car model to obtain the vehicle's real-time sideslip angle. yaw rate Longitudinal speed The rotational angular velocity of the steering motor is obtained through the steer-by-wire system. Actual front wheel steering angle Three-phase current of the motor , , State variables; through Clark and Park transformations, the three-phase AC current... , , Convert to shaft current and shaft current Then, based on the vehicle's dynamics mechanism, an integrated two-degree-of-freedom dynamic model of the vehicle and a dynamic model of the steer-by-wire actuator are established. A centralized control model of the SBW system based on the PMSM voltage equations in the coordinate system; finally, based on the given longitudinal vehicle speed... and ideal front wheel steering angle To obtain the ideal yaw rate Ideal centroid side slip angle Ideal shaft current and voltage and ideal shaft voltage ;

[0024] The specific implementation method is as follows:

[0025] 1.1 Centralized Control Model of SBW System

[0026] 1.1.1 Two-degree-of-freedom dynamic model of the vehicle

[0027] Using a classic two-degree-of-freedom vehicle model, neglecting the influence of vertical loads and considering only the yaw and lateral degrees of freedom, the two-degree-of-freedom dynamic model of the vehicle can be expressed as:

[0028] (1)

[0029] In the formula, For the overall vehicle quality, For lateral velocity, , , , , , , , These represent the longitudinal and lateral forces acting on the four wheels, respectively. The yaw rate is angular velocity. For vehicles to bypass Moment of inertia of the shaft , These are the distances from the vehicle's center of gravity to the front and rear axles, respectively. The track width is the distance between the front and rear wheels. This is the actual front wheel steering angle.

[0030] For ease of study, we assume the vehicle steering angle input is sufficiently small. , Assume the longitudinal velocity of the vehicle during its journey is... If the tire lateral slip characteristics remain constant and within the linear range, then the lateral forces acting on the front and rear tires are:

[0031] (2)

[0032] In the formula, , These are the lateral stiffness of the front and rear wheels, respectively. , These are the slip angles of the front and rear tires, respectively.

[0033] (3)

[0034] In the formula, The sideslip angle is the angle of the centroid. , This refers to the longitudinal vehicle speed.

[0035] Substituting equations (2) and (3) into the two-degree-of-freedom dynamics model (1) of the vehicle, we get:

[0036] (4)

[0037] In the formula, intermediate variables , , , , , All intermediate variables or intermediate vectors in this invention refer to auxiliary variables introduced during modeling, derivation, calculation, or algorithm implementation to connect inputs and outputs, simplify expressions, and decompose complex problems, and have no actual physical meaning.

[0038] Ideal centroid sideslip angle Ideal yaw rate and ideal front wheel steering angle Satisfying the same two-degree-of-freedom vehicle dynamics model:

[0039] (5)

[0040] Let the centroid side slip angle deviation yaw rate deviation Front wheel steering angle deviation The following is the equation for the two-degree-of-freedom dynamic deviation of the vehicle:

[0041] (6)

[0042] 1.1.2 Dynamic Model of Steering Actuator in SBW System

[0043] The SBW (Steering Wheel Welding) system's steering actuator is an electromechanical coupling system consisting of a motor, reducer, rack and pinion gears, and steering knuckle. Its core function is to convert the motor's rotational motion into wheel angle, accurately reflecting inertia, damping, stiffness, friction, self-centering torque, and backlash nonlinear characteristics. The dynamic model of the SBW system's steering actuator is as follows:

[0044] (7)

[0045] In the formula, , For the rotational inertia of the front wheels and steering motor, , For the damping coefficient of the front wheel and steering motor, For the overall transmission ratio, Main sales friction, For tire trail, For mechanical trailing distance, for shaft current, This refers to the number of pole pairs in a PMSM motor. For the equivalent flux linkage of a permanent magnet, intermediate variables , , , .

[0046] , , and Since the same dynamic model of the steering actuator of the SBW system is satisfied, the ideal can be obtained. shaft current :

[0047] (8)

[0048] make Shaft current deviation The dynamic deviation model of the steering actuator of the SBW system can be obtained as follows:

[0049] (9)

[0050] 1.1.3 PMSM voltage equations in coordinate system

[0051] Because PMSMs have advantages such as high efficiency and low output torque ripple, SBW systems often use surface-mounted PMSMs as steering actuators. The voltage equation in the coordinate system is:

[0052] (10)

[0053] In the formula, for shaft current, for shaft current, For resistance, For equivalent inductance, , They are respectively axis, shaft voltage, The rotational angular velocity of the motor is used to drive the steering. This is the equivalent magnetic flux linkage.

[0054] Based on the PMSM voltage equation in the dq coordinate system, using ideal shaft current ,ideal shaft current (This invention) ), can be obtained Ideal voltage in coordinate system and :

[0055] (11)

[0056] make Shaft current deviation , Shaft voltage deviation , Shaft voltage deviation , can be obtained PMSM voltage deviation equation in coordinate system:

[0057] (12)

[0058] 1.1.4 Centralized Control Model of SBW System

[0059] Let the state vector Theoretical design of control vectors By rearranging formulas (6), (9), and (12), we can obtain the centralized control model of the SBW system:

[0060] (13)

[0061] In the formula, the state transition matrix and control input matrix for

[0062] (14)

[0063] (15)

[0064] Considering the parameter perturbation terms or modeling error terms that exist in the actual operation of the SBW system. and external disturbances or unmodeled dynamic terms Then the centralized control model formula (13) of the SBW system can be rewritten as follows:

[0065] (16)

[0066] In the formula, , , and All are interference items. , , and For positive integers, respectively represent and The known upper bound of .

[0067] Step 2: Design the non-singular state transformation matrix Non-singular state transformation is performed on the established centralized control model of the SBW system to achieve dimensionality reduction and decoupling of the model. The subsystems after dimensionality reduction and decoupling are divided into controllable subsystems and internal subsystems.

[0068] The specific implementation method is as follows:

[0069] As can be seen from formula (16), the model has a high degree of integration, but the increase in model dimension leads to an increase in computational load, which increases the difficulty of controller design. Therefore, this invention designs a non-singular state transformation matrix and uses this matrix to perform non-singular state transformation on formula (16) to achieve dimensionality reduction and decoupling of the model.

[0070] 2.1 Non-singular state transformation matrix

[0071] Design the nonsingular state transformation matrix. :

[0072] (17)

[0073] In the formula, , , , , , , , , , , , , , , , As an intermediate variable:

[0074] (18)

[0075] (19)

[0076] (20)

[0077] (twenty one)

[0078] (twenty two)

[0079] (twenty three)

[0080] (twenty four)

[0081] (25)

[0082] (26)

[0083] (27)

[0084] (28)

[0085] (29)

[0086] (30)

[0087] 2.2 Non-singular transformations

[0088] Using the non-singular state transformation matrix formula (17), the centralized control model formula (16) of the SBW system is transformed into a non-singular state transformation formula (31):

[0089] (31)

[0090] in, This is the new state vector after the non-singular state transformation. , for State variables, ;

[0091] Through non-singular state transformation, formula (16) is decomposed into 5 independent subsystem formulas (32)-(36). It can be seen that... It appears only in the last layer. All other layers can use the state variables of the previous layer as virtual controls, so that each subsystem can be controlled independently, realizing the dimensionality reduction and decoupling of the model. To facilitate the design of subsequent control algorithms, we define formulas (32)-(35) as internal subsystems and formula (36) as controllable subsystems.

[0092] (32)

[0093] (33)

[0094] (34)

[0095] (35)

[0096] (36)

[0097] In the formula, , , and For the system design parameters, the other parameters in equation (36) are designed as follows:

[0098] (37)

[0099] (38)

[0100] (39)

[0101] (40)

[0102] (41)

[0103] (42)

[0104] in, , , , , , .

[0105] Step 3: First, based on the centroid sideslip angle obtained in Step 1... yaw rate Longitudinal speed ; Rotational angular velocity of the steering actuator motor Actual front wheel steering angle , shaft current , shaft current Ideal front wheel steering angle Ideal yaw rate Ideal centroid side slip angle Ideal shaft current , shaft voltage Ideal shaft voltage and the given longitudinal speed The control voltage required by the steering actuator motor is obtained through the interference observer module and the integral terminal sliding mode control module of the energy control subsystem. and the state variables of the controllable subsystem Then, through the parameter design module of the internal subsystem, reasonable design parameters are selected according to the system performance requirements while ensuring the controllability of the internal subsystem. Combined with the state variables of the energy control subsystem This allows the state variables of the internal subsystems to gradually converge to zero in a hierarchical order; finally, the control voltage required for the steering actuator motor is... After inverse Park converter, SVPWM module and inverter, it is converted into the three-phase AC voltage required by the steering actuator motor. , , It is then transmitted to the steer-by-wire system, which generates the actual front wheel steering angle. And then it is delivered to CarSim car models.

[0106] The specific implementation method is as follows:

[0107] 3.1 Integral Terminal Sliding Mode Control Based on Disturbance Observer

[0108] Integral-type terminal sliding mode improves system robustness and control accuracy by introducing an integral term to eliminate the stage where the system state approaches the sliding surface. However, the integral term inevitably generates overshoot when the state changes significantly. To avoid the problems of integral saturation-induced integral windup, increased overshoot, and prolonged settling time, this invention first designs the integral-type terminal sliding surface function as follows:

[0109] (43)

[0110] in, , , , , , , , The design parameters are all positive odd numbers, satisfying... , ; For time, For symbolic functions, It is a saturation function. The deviation between the theoretically designed control vector and the actual control vector constrained by the three-phase AC voltage amplitude. , and for State variables, , and for State variables, , , , , , .

[0111] and Definition:

[0112] (44)

[0113] In the formula, The boundary layer threshold, , , They are respectively The upper limit of amplitude. (Through) Dynamically limiting integral growth avoids integral drift without affecting convergence speed, thus solving the problem of the mutual constraint between anti-saturation and fast convergence.

[0114] Design the following adaptive reaching law:

[0115] (45)

[0116] In the formula, the power , and for The maximum and minimum values, Design parameters , , , , .

[0117] By embedding a preset maximum convergence time , Design power Adaptive rate, :

[0118] (46)

[0119] In the formula, the omission coefficient Base power Design parameters , , .

[0120] Then, to facilitate controller design, a new control vector is defined. :

[0121] (47)

[0122] In equation (25) Consider it as a joint disturbance of the energy control subsystem ,make Assuming The change is slow, that is... .make for The estimated value is based on the integral terminal sliding surface function formula (43) and its adaptive reaching law formula (45), and the power law formula (45). The adaptive rate formula (46) is used to design... as follows:

[0123] (48)

[0124] The intermediate vector in formula (48):

[0125] (49)

[0126] (50)

[0127] (51)

[0128] Finally, the interference observer formula (52) is designed to obtain the result in formula (48). estimated value :

[0129] (52)

[0130] in, For the observer's state variables, This is the observer gain.

[0131] According to formulas (47) and (48), the theoretically designed control vector is obtained. According to formula (11), we obtain Finally, based on Calculate the control voltage required for the steering actuator motor. .

[0132] 3.2 Parameter Design

[0133] For the internal subsystem formulas (32)-(35), , Designed as Then, in the state variables of the controllable subsystem After reaching the equilibrium point, the state variables of the internal subsystem It can asymptotically approach the equilibrium point in a hierarchical order, effectively ensuring the controllability of the internal subsystems.

Claims

1. A centralized control method for a steer-by-wire system based on non-singular state transformation, characterized in that, The method includes the following steps: Step 1: First, use the CarSim car model to obtain the vehicle's real-time sideslip angle, yaw rate, and longitudinal speed; then, use the steer-by-wire system to obtain the steering motor's rotational angular velocity, actual front wheel angle, and the motor's three-phase current; finally, use Clark and Park transformations to convert the three-phase AC current into... shaft current and Axis current; then, based on the vehicle's dynamic mechanism, an integrated two-degree-of-freedom dynamic model of the vehicle and a dynamic model of the steer-by-wire actuator are established. A centralized control model of the SBW system based on the PMSM voltage equation in the coordinate system; finally, based on the given longitudinal vehicle speed and ideal front wheel steering angle, the ideal yaw rate, ideal center of gravity sideslip angle, and ideal... Axis current and voltage, ideal Shaft voltage; Step 2: Design a non-singular state transformation matrix to perform non-singular state transformation on the established centralized control model of the SBW system, thereby achieving dimensionality reduction and decoupling of the model. The subsystems after dimensionality reduction and decoupling are then divided into controllable subsystems and internal subsystems. Step 3: First, based on the sideslip angle, yaw rate, and longitudinal speed obtained in Step 1; the rotational angular velocity of the steering actuator motor, and the actual front wheel steering angle... shaft current, Axis current, ideal front wheel steering angle, ideal yaw rate, ideal center of gravity sideslip angle, ideal Axis current and voltage, ideal Given the axle voltage and the longitudinal vehicle speed, the control voltage required by the steering actuator motor is obtained through the disturbance observer module and the integral terminal sliding mode control module of the energy control subsystem. and the state variables of the controllable subsystem Then, through the parameter design module of the internal subsystem, reasonable design parameters are selected according to the system performance requirements while ensuring the controllability of the internal subsystem. Combined with the state variables of the energy control subsystem This allows the state variables of the internal subsystems to gradually converge to zero in a hierarchical order; finally, the control voltage required for the steering actuator motor is... After passing through inverse Park converter, SVPWM module and inverter, the voltage is converted into the three-phase AC voltage required by the steering actuator motor, and then sent to the steer-by-wire system, which generates the actual front wheel steering angle. And then it is delivered to CarSim car models.

2. The centralized control method for a steer-by-wire system based on non-singular state transformation as described in claim 1, characterized in that, Step one involves establishing an integrated two-degree-of-freedom dynamic model of the vehicle and a dynamic model of the steer-by-wire actuator based on the vehicle's dynamics mechanism. A centralized control model of the SBW system based on the PMSM voltage equations in the coordinate system; definition and These are the centroid sideslip angle and its ideal value, respectively. and The yaw rate and its ideal value are respectively. and These are the actual front wheel steering angle and its ideal value, respectively. For longitudinal vehicle speed, , These are the lateral stiffness of the front and rear wheels, respectively. For the overall vehicle quality, For vehicles to bypass Moment of inertia of the shaft , These are the distances from the vehicle's center of gravity to the front and rear axles, respectively. , For the rotational inertia of the front wheels and steering motor, , For the damping coefficient of the front wheel and steering motor, For the overall transmission ratio, For tire trail, For mechanical trailing distance, This refers to the number of pole pairs in a PMSM motor. The equivalent magnetic flux of a permanent magnet. Main sales friction, and They are respectively Shaft current and its ideal value; and They are respectively shaft current and its ideal value, and They are respectively Shaft voltage and its ideal value, and They are respectively Shaft voltage and its ideal value, For resistance, For equivalent inductance, The rotational angular velocity of the motor is used to determine the direction of rotation; Define state variables , , , ; Control variables , ; intermediate variables , , , , , ; , , , All intermediate variables in this invention refer to auxiliary variables introduced during modeling, derivation, calculation, or algorithm implementation to connect input and output, simplify expression, and decompose complex problems; they have no actual physical meaning. First, establish the two-degree-of-freedom dynamic deviation equations for the vehicle: （1） Secondly, the dynamic deviation equation of the steering actuator of the SBW system is established: （2） In the formula, ; Again, establish PMSM voltage deviation equation in coordinate system: （3） In the formula, , , ; Finally, let the state transition matrix... and control input matrix for: ， （4） Considering the parameter perturbation terms or modeling error terms that exist in the actual operation of the SBW system. and external disturbances or unmodeled dynamic terms Let the state vector Theoretical design of control vectors Combining formulas (1)-(3), the centralized control model of the SBW system can be obtained: （5） In the formula, , , and All are interference items. , , and For positive integers, respectively represent and The known upper bound of .

3. The centralized control method for a steer-by-wire system based on non-singular state transformation as described in claim 1, characterized in that, Step two describes the design of the non-singular state transformation matrix. Non-singular state transformation is performed on the established centralized control model of the SBW system to achieve dimensionality reduction and decoupling of the model. The subsystems after dimensionality reduction and decoupling are divided into controllable subsystems and internal subsystems. First, design the non-singular state transformation matrix. as follows: （6） In the formula, , , , , , , , , , , , , , , , As an intermediate variable: （7） （8） （9） （10） （11） （12） （13） （14） （15） （16） （17） （18） （19） Then, using the non-singular state transformation matrix formula (6), the centralized control model formula (5) of the SBW system is transformed into a non-singular state: （20） In the formula, This is the new state vector after the non-singular state transformation. , for State variables, ; Through non-singular state transformation, equation (5) is decomposed into 5 independent subsystems: （21） （22） （23） （24） （25） In the formula, , , and For system design parameters, the other intermediate variables in formula (25) are designed as follows: （26） （27） （28） （29） （30） （31） in, , , , , ; As can be seen, control vector It appears only in the last layer. All other layers can use the state variables of the previous layer as virtual control variables, so that each subsystem can be controlled independently, realizing the dimensionality reduction and decoupling of the model. To facilitate the design of subsequent control algorithms, we define formulas (21)-(24) as internal subsystems and formula (25) as controllable subsystems.

4. The centralized control method for a steer-by-wire system based on non-singular state transformation as described in claim 1, characterized in that, Step three describes obtaining the control voltage required by the steering actuator motor through the interference observer module and the integral terminal sliding mode control module of the energy control subsystem. and the state variables of the controllable subsystem ; By using the parameter design module of the internal subsystem, reasonable design parameters can be selected based on system performance requirements while ensuring the controllability of the internal subsystem. Combined with the state variables of the energy control subsystem This allows the state variables of the internal subsystems to gradually converge to zero in a hierarchical order. First, design the integral terminal sliding surface function: （32） in, , , , , , , , The design parameters are all positive odd numbers, satisfying... , ; For time, For symbolic functions, , For the theoretically designed control quantity, It is a saturation function. This is the actual control quantity limited by the amplitude of the three-phase AC voltage. and The definition is as follows: ， （33） In the formula, This is the boundary layer threshold; This controls the upper limit of the vector magnitude; pass Dynamically limiting integral growth avoids integral drift without affecting convergence speed, thus solving the problem of the mutual constraint between anti-saturation and fast convergence. Secondly, an adaptive reaching law is designed for the integral terminal sliding surface: （34） In the formula, the power , and for The maximum and minimum values, Design parameters , , , , ; By embedding a preset maximum convergence time , Design power Adaptive rate, : （35） In the formula, the omission coefficient Base power Design parameters , , ; Secondly, based on the theoretically designed control vector Define a new control vector : （36） In formula (25) Consider it as a joint disturbance of the energy control subsystem ,make , assuming The change is slow, that is... ; make for The estimated value is based on the integral terminal sliding surface function formula (32) and its adaptive reaching law formula (34), and the power law formula (35). Adaptive rate formula (35), design a new control vector as follows: （37） Intermediate variables in the formula: （38） （39） （40） Next, design the interference observer formula (41) to obtain The estimated value : （41） In the formula, For the observer's state variables, For observer gain; According to formulas (36) and (37), the theoretically designed control vector can be obtained. According to the ideal shaft voltage and shaft voltage , The control voltage required for the steering actuator motor can be obtained. ; Finally, parameter design is performed, and... , Designed as Then, in the state variables of the controllable subsystem After reaching the equilibrium point, the state variables of the internal subsystem It can asymptotically approach the equilibrium point in a hierarchical order, effectively ensuring the controllability of the internal subsystems.

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