A synchronous tracking control system and method for a dual-motor steer-by-wire system

Abstract

This invention discloses a synchronous tracking control system and method for a dual-motor steer-by-wire system, comprising: establishing a model of the dual-motor steer-by-wire system; calculating the target front wheel steering angle to maintain vehicle stability; adjusting the target steering angle signals of the two steering motors in real time based on torque feedback control principles and a virtual master shaft model; estimating the nonlinear state functions of the state equations of the two steering motors in real time based on an adaptive radial basis function neural network, and calculating the current control signals of the two steering motors based on this; and calculating the current compensation control signals of the two steering motors based on the mean-deviation coupling synchronous control and variable approach law sliding mode control principles. This invention can effectively cope with perturbations and uncertain disturbances in the model parameters of the steering motors, ensuring the synchronous tracking performance of the dual-motor steer-by-wire system.

Description

Technical Field

[0001] This invention belongs to the field of steer-by-wire technology, specifically relating to a synchronous tracking control system and method for a dual-motor steer-by-wire system. Background Technology

[0002] With the continuous development of intelligent and electrification technologies, steer-by-wire systems have become an important technological development direction in this field due to their simplified mechanical structure, excellent dynamic response characteristics, and ease of integration with advanced control algorithms. Steer-by-wire systems employing a dual-motor architecture significantly enhance system reliability and fault tolerance by introducing redundant actuators, effectively avoiding steering function failure caused by a single motor malfunction. Simultaneously, the dual-motor cooperative drive design greatly improves the system's output torque and load capacity, better meeting the stringent requirements for high reliability and high steering performance.

[0003] However, in actual operation, complex and variable operating conditions lead to significant parameter perturbations in the steering motor dynamics model. Simultaneously, the nonlinear restoring torque generated by the interaction between the tires and the ground further introduces strong uncertainty disturbances, posing a severe challenge to the precise tracking control of a single motor. More critically, the aforementioned multi-source disturbances, such as model parameter perturbations and uncertainty disturbances, exhibit differentiated coupling effects in the dual-motor system, directly causing inconsistencies in the output torque and steering angle response of the two motors. This results in cumulative tracking errors and systematic synchronization errors at the steering execution level.

[0004] Currently, research on control of dual-motor steer-by-wire systems mainly focuses on fault-tolerant mechanism design or single disturbance compensation. For example, Chinese invention patent application CN202211300057.4, entitled "A Safety Redundancy Dual-Motor Steering System for Electric Vehicles and Its Control Method," proposes a dual-motor steering system and its control strategy with a safety redundancy switching mechanism, emphasizing functional maintenance after a fault. Chinese invention patent application CN202311668938.6, entitled "A Dual-Motor Steer-by-Wire System and Its Synchronization Control Method," introduces a virtual master shaft synchronization control structure to suppress synchronization deviations caused by communication delays. However, these solutions do not fully consider the coupling characteristics of parameter perturbations and nonlinear disturbances between the two motors. Especially under extreme conditions, relying solely on a virtual master shaft or independent disturbance compensation is insufficient to effectively suppress systematic synchronization errors caused by multi-source disturbance coupling, leading to decreased control accuracy and insufficient robustness. Summary of the Invention

[0005] In view of the shortcomings of the prior art, the purpose of this invention is to provide a synchronous tracking control system and method for a dual-motor steer-by-wire system, so as to solve the problem of insufficient angle tracking performance and synchronization performance of the dual-motor steer-by-wire system caused by multiple sources of disturbance such as motor parameter perturbation and uncertainty disturbance in the existing dual-motor steer-by-wire system technology.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] The present invention provides a synchronous tracking control system for a dual-motor steer-by-wire system, comprising: a vehicle stability control unit, a steering wheel assembly unit, an electronic control unit, and a steering actuator;

[0008] The vehicle stability control unit includes: a vehicle status information sensor and a vehicle stability controller;

[0009] Vehicle status information sensors are used to collect the status information of the vehicle, including the front wheel steering angle, center of gravity sideslip angle, and yaw rate.

[0010] The vehicle stability controller is used to assist the driver in controlling the vehicle to ensure the stability of the vehicle during operation.

[0011] The steering wheel assembly unit includes: a steering wheel, a steering column, a steering wheel angle sensor, a road feel motor sensor, a road feel motor, a road feel motor reducer, and a road feel motor driver.

[0012] The steering wheel angle sensor is used to collect the steering commands generated by the driver through the steering wheel and steering column, and convert them into electronic steering commands and transmit them to the electronic control unit.

[0013] The road sensor is used to collect the current and rotation angle signals of the road sensor motor and transmit them to the electronic control unit.

[0014] The road feel motor driver is used to receive control signals sent by the electronic control unit and control the road feel motor to generate simulated road feedback torque. The road feedback torque is fed back to the driver through the road feel motor reducer, steering column and steering wheel.

[0015] The electronic control unit includes: a steering motor angle tracking controller A, a steering motor angle tracking controller B, a virtual spindle synchronization controller, a mean deviation coupling synchronization controller, and a road feel motor controller.

[0016] Steering motor angle tracking controller A and steering motor angle tracking controller B are used to track the electronic steering commands sent by the steering wheel angle sensor and generate control signals for steering motor driver A and steering motor driver B in the steering actuator.

[0017] The virtual spindle synchronization controller is used to calculate the virtual torque signal and adjust the target angle signals of steering motor A and steering motor B in the steering actuator in real time according to the virtual torque signal to reduce the angle synchronization error between the two motors.

[0018] The mean deviation coupling synchronization controller is used to send current compensation control signals to the steering motor angle tracking controller A and the steering motor angle tracking controller B respectively based on the mean deviation of the steering angle of steering motor A and steering motor B, so as to eliminate the steering angle synchronization error of steering motor A and steering motor B.

[0019] The road sensor motor controller is used to generate control commands to control the road sensor motor driver to drive the road sensor motor, so as to simulate the road feedback torque;

[0020] The steering actuator includes: a steering motor driver A, a steering motor A, a steering motor reducer A, a pinion A, a steering motor angle sensor A, a steering motor current sensor A, a steering motor driver B, a steering motor B, a steering motor reducer B, a pinion B, a steering motor angle sensor B, a steering motor current sensor B, a rack, a steering tie rod, a left front wheel, and a right front wheel.

[0021] Steering motor driver A and steering motor driver B, respectively, complete the angle tracking control of steering motor A and steering motor B based on the control signals of steering motor angle tracking controller A and steering motor angle tracking controller B;

[0022] Steering motor A is connected to pinion A via steering motor reducer A, and steering motor B is connected to pinion B via steering motor reducer B. Pinion A and pinion B mesh with rack, and rack is connected to steering tie rod. Steering tie rod is connected to the left front wheel and the right front wheel respectively. Steering motor A and steering motor B work together to drive the steering of the left front wheel and the right front wheel.

[0023] Steering motor angle sensor A and steering motor current sensor A are installed on steering motor A; steering motor angle sensor A is used to collect the steering motor angle signal and send it to steering motor angle tracking controller A; steering motor current sensor A is used to collect the steering motor angle signal and send it to steering motor angle tracking controller A.

[0024] Steering motor angle sensor B and steering motor current sensor B are installed on steering motor B; steering motor angle sensor B is used to collect the steering motor B's angle signal and send it to steering motor angle tracking controller B; steering motor current sensor B is used to collect the steering motor B's current signal and send it to steering motor angle tracking controller B.

[0025] Furthermore, the virtual spindle synchronization controller includes a virtual spindle for providing the same target angle signal to steering motor A and steering motor B.

[0026] Furthermore, the virtual torque calculator A in the virtual spindle synchronous controller calculates the virtual torque A based on the angle signal collected by the motor angle sensor A and the target angle signal output by the virtual spindle; the virtual torque calculator B calculates the virtual torque B based on the angle signal collected by the motor angle sensor B and the target angle signal output by the virtual spindle.

[0027] Furthermore, the steering motor angle tracking controller A includes: an adaptive radial basis neural network A and a fast terminal sliding mode controller A; the adaptive radial basis neural network A estimates the nonlinear state function of the steering motor A in real time based on the angle signal collected by the motor angle sensor A, and adaptively adjusts the state equation of the steering motor A; the fast terminal sliding mode controller A calculates the current control signal for the steering motor driver A based on the error between the angle signal collected by the motor angle sensor A and the target signal, so as to control the steering motor A.

[0028] Furthermore, the steering motor angle tracking controller B includes: an adaptive radial basis neural network B and a fast terminal sliding mode controller B; the adaptive radial basis neural network B estimates the nonlinear state function of the steering motor B in real time based on the angle signal collected by the motor angle sensor B, and adaptively adjusts the state equation of the steering motor B; the fast terminal sliding mode controller B calculates the current control signal for the steering motor driver B based on the error between the angle signal collected by the motor angle sensor B and the target signal, so as to control the steering motor B.

[0029] Furthermore, the steering motor A and steering motor B are the same model of motor.

[0030] The present invention provides a synchronous tracking control method for a dual-motor steer-by-wire system, based on the aforementioned system, comprising the following steps:

[0031] Step 1: Establish a model of the dual-motor steer-by-wire system, including: virtual spindle model, steering motor model, gear and rack model, and nonlinear tire model;

[0032] Step 2: Using the vehicle status information and steering wheel commands, and based on the vehicle stability control principle, calculate the target front wheel steering angle to maintain vehicle stability;

[0033] Step 3: Using the target front wheel angle, the virtual torque A of steering motor A and the virtual torque B of steering motor B, based on the torque feedback control principle and the virtual spindle model, adjust the target angle signals of steering motor A and steering motor B in real time.

[0034] Step 4: Using the error signal between the motor angle signals collected by motor angle sensor A and motor angle sensor B and the target angle signals of steering motor A and steering motor B, the nonlinear state functions of the state equations of steering motor A and steering motor B are estimated in real time based on an adaptive radial basis neural network.

[0035] Step 5: Based on the target angle signals of steering motor A and steering motor B in Step 3, the nonlinear state functions of the state equations of steering motor A and steering motor B in Step 4, and the motor angle signals collected by motor angle sensor A and motor angle sensor B, calculate the current control signals of steering motor A and steering motor B respectively.

[0036] Step 6: Based on the motor angle signals collected by motor angle sensor A and motor angle sensor B, calculate the mean angle deviation of steering motor A and steering motor B. Based on the variable approach law sliding mode control principle and steering motor model, calculate the current compensation control signal of steering motor A and steering motor B. Eliminate the angle synchronization error between steering motor A and steering motor B according to the obtained current compensation control signal.

[0037] Step 7: Using the current control signals of steering motor A and steering motor B in step 5 and the current compensation control signals of steering motor A and steering motor B in step 6, drive steering motor A and steering motor B to track the target turning angle signal; steering motor A and steering motor B drive the rack to move through steering motor reducer A and pinion A and steering motor reducer B and pinion B respectively, and then control the left front wheel and right front wheel to complete the steering through the steering tie rod.

[0038] Furthermore, step 1 specifically includes:

[0039] The virtual spindle model is as follows:

[0040] (1);

[0041] In the formula, The moment of inertia of the virtual principal axis; The rotation angle of the virtual spindle; The damping coefficient of the virtual spindle; The virtual torque is A; The virtual torque is B; The virtual torque output by the virtual spindle;

[0042] The steering motor model is as follows:

[0043] (2);

[0044] In the formula, Let i be the moment of inertia of the steering motor i; Let i be the mechanical angular velocity of the steering motor i; Let be the damping coefficient of steering motor i; The load torque of steering motor i; The electromagnetic torque of steering motor i; Let i be the number of pole pairs of the steering motor i; This is the current control signal for steering motor i; Let be the permanent magnet flux linkage of steering motor i; where i = 1, 2, corresponding to steering motor A and steering motor B respectively;

[0045] The gear and rack model is as follows:

[0046] (3);

[0047] In the formula, The mass of the rack; This represents the displacement of the rack; is the damping coefficient of the rack; The steering resistance acting on the rack by the front wheels; Coulomb friction; , These are the reduction ratios of steering motor reducer A and steering motor reducer B, respectively. , These are the load torques of steering motor A and steering motor B, respectively. , These are the shaft stiffnesses of steering motor A and steering motor B, respectively. , These are the mechanical rotation angles of steering motor A and steering motor B, respectively. , These are the radii of pinion A and pinion B, respectively.

[0048] The nonlinear tire model is as follows:

[0049] (4);

[0050] In the formula, The lateral force of each front tire; The lateral stiffness of each front wheel; The slip angle of each front tire; The slip ratio of each front wheel tire; It is a boundary function; The coefficient of friction utilization; The road surface adhesion coefficient; For each front tire; These are the coefficients of the tire dynamic parameter model; The longitudinal speed of each front tire; For the longitudinal stiffness of each front tire; The steering resistance exerted by each front tire on the rack; and These are the mechanical trail and pneumatic trail of each front wheel, respectively.

[0051] Furthermore, step 3 specifically includes:

[0052] Step 31: Calculate virtual torque A and virtual torque B, as shown in the following expressions:

[0053] (5);

[0054] In the formula, The virtual torque is A; The virtual torque is B; This is the virtual feedback stiffness coefficient; This is the virtual steering angle correction coefficient for steering motor A;

[0055] Step 32: Calculate the virtual torque of the virtual spindle, using the following formula:

[0056] (6);

[0057] In the formula, The virtual stiffness coefficient of the virtual principal axis; The rotation angle of the virtual spindle is calculated based on the overall reduction ratio of the steering system and the rotation angle of the target wheel.

[0058] Combining equations (1) and (6), the output of the virtual spindle synchronous controller is calculated as follows:

[0059] (7);

[0060] The virtual spindle controls the output angle of the virtual spindle based on the virtual torque A and virtual torque B output by the virtual torque calculator A and virtual torque calculator B. This angle serves as the target angle for the steering motors A and B, thereby reducing the synchronization error between the steering motors A and B from the perspective of tracking the target.

[0061] Furthermore, step 4 specifically includes:

[0062] Step 41: Establish the state equation of steering motor i based on equations (2) and (3), as follows:

[0063] (8);

[0064] In the formula, Let i be the nonlinear state function of the steering motor i. ; Let i be the state vector of the steering motor i. , For the steering motor i's angle tracking error, ,in The target steering angle signal for steering motor i. The steering angle sensor i collects the steering angle signal of the steering motor; This is the derivative of the steering angle tracking error of steering motor i; For the system control gain of steering motor i, ; This is the control current signal for steering motor i; For the load interference of steering motor i, , , where D is the upper limit of the load torque of steering motor A and steering motor B;

[0065] Step 42: Design the input and output of the adaptive radial basis function neural network i, as follows:

[0066] (9);

[0067] In the formula, and These are the center position and width of neuron j, respectively; This is the output of the Gaussian function; For ideal neural network weights; For the approximation error of the neural network, , This is the upper bound of the estimation error of the neural network; the adaptive radial basis neural network i is either adaptive radial basis neural network A or adaptive radial basis neural network B;

[0068] Step 43: Estimating the nonlinear state function of steering motor i using the adaptive radial basis function neural network i for:

[0069] (10);

[0070] In the formula, for The transpose of the matrix; For weight estimation of ideal neural network weights, a design based on the Lyapunov stability principle is proposed. , These are adaptive coefficients; For the sliding surface of the fast terminal sliding mode controller i, , For linear term gain coefficients, The gain coefficient for the nonlinear term. for The exponent of the power is q, where q is the numerator of the fractional power and p is the denominator of the fractional power. Both p and q are positive odd numbers, and p > q.

[0071] An adaptive radial basis neural network i is used to estimate the nonlinear state function, thus avoiding the problem of decreased tracking accuracy caused by model parameter perturbation of steering motor i under complex operating conditions.

[0072] Furthermore, step 5 specifically includes:

[0073] Step 51: Design the fast terminal sliding surface i of the fast terminal sliding controller i as follows:

[0074] (11);

[0075] In the formula, The gain coefficient for the linear term; q is the gain coefficient of the nonlinear term; q is the numerator of the fractional power, p is the denominator of the fractional power, both p and q are positive odd numbers, and p > q;

[0076] Step 52: Based on the nonlinear state function of the steering motor i estimated by the fast terminal sliding surface i and the adaptive radial basis neural network, calculate the final control current signal of the fast terminal sliding controller i. ,as follows:

[0077] (12);

[0078] In the formula, This is the sliding mode gain coefficient; This is the switching gain coefficient;

[0079] The fast terminal sliding mode controller i outputs the final control current signal to the steering motor i to ensure that the steering motor i can track the target angle signal under the perturbation of model parameters.

[0080] Furthermore, step 6 specifically includes:

[0081] Step 61: Design the variable approach law sliding surface as follows:

[0082] (13);

[0083] In the formula, Let be the system state vector, and satisfy . ; and For control coefficients, , ; Let be the smoothing coefficients of the hyperbolic tangent function, satisfying ; For the power exponent, satisfying ; As the benchmark weighting coefficient, satisfying ; This is the state gain adjustment coefficient; It is a state index; The width parameter of the hyperbolic secant function;

[0084] Step 62: Calculate the mean deviation of the steering angle of steering motor i, as follows:

[0085] (14);

[0086] In the formula, and These are the mean deviations of the steering angles of steering motor A and steering motor B, respectively. The average steering angle of steering motor A and steering motor B;

[0087] Step 63: Design the integral sliding surface for:

[0088] (15);

[0089] In the formula, This is the integral gain coefficient;

[0090] Step 64: Based on the integral sliding surface, calculate the current compensation control signal for steering motor A and the current compensation control signal for steering motor B output by the mean deviation coupled synchronous controller as follows:

[0091] (16);

[0092] in, These are the current compensation control signals for steering motor A and steering motor B.

[0093] The beneficial effects of this invention are:

[0094] 1. This invention not only solves the problem of decreased tracking performance of steering motor caused by multiple sources of disturbance such as perturbation of system model parameters and uncertainty disturbance, but also effectively improves the synchronization performance of dual-motor steer-by-wire system.

[0095] 2. This invention estimates the nonlinear state function through a neural network to eliminate model parameter perturbations; and utilizes the switching term of sliding mode control to suppress uncertainty disturbances, effectively improving the tracking accuracy of the steering motor under uncertain operating conditions.

[0096] 3. This invention adjusts the target rotation angle signal through a virtual spindle synchronous controller and uses a mean deviation coupled synchronous controller for current compensation to directly correct the motor rotation angle. By combining target adjustment with real-time compensation, this invention effectively improves the synchronization accuracy and dynamic response performance of the dual-motor steer-by-wire system. Attached Figure Description

[0097] Figure 1 This is a schematic diagram of the system structure of the present invention;

[0098] Figure 2 This is a flowchart of the method of the present invention;

[0099] Figure 3 This is a schematic diagram of the signal flow of the method of the present invention;

[0100] Figure 1 In the diagram, 1: Steering wheel, 2: Road feel motor sensor, 3: Road feel motor, 4: Road feel motor driver, 5: Steering motor A, 6: Steering motor angle sensor A, 7: Steering motor current sensor A, 8: Steering motor A driver, 9: Steering tie rod, 10: Left front wheel, 11: Pinion A, 12: Steering motor reducer A, 13: Rack, 14: Pinion B, 15: Steering motor reducer B, 16: Right front wheel, 17: Steering motor driver B, 18: Steering motor angle sensor B, 19: Steering motor current sensor B, 20: Steering motor B, 21: Electronic control unit, 22: Road feel motor reducer, 23: Steering column, 24: Steering wheel angle sensor. Detailed Implementation

[0101] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to embodiments and accompanying drawings. The content mentioned in the embodiments is not intended to limit the present invention.

[0102] Reference Figure 1 As shown, the present invention provides a synchronous tracking control system for a dual-motor steer-by-wire system, comprising: a vehicle stability control unit, a steering wheel assembly unit, an electronic control unit, and a steering actuator;

[0103] The vehicle stability control unit includes: a vehicle status information sensor and a vehicle stability controller;

[0104] Vehicle status information sensors are used to collect the status information of the vehicle, including the front wheel steering angle, center of gravity sideslip angle, and yaw rate.

[0105] The vehicle stability controller is used to assist the driver in controlling the vehicle to ensure the stability of the vehicle during operation.

[0106] The steering wheel assembly unit includes: a steering wheel 1, a steering column 23, a steering wheel angle sensor 24, a road feel motor sensor 2, a road feel motor 3, a road feel motor reducer 22, and a road feel motor driver 4.

[0107] Steering wheel angle sensor 24 is used to collect steering commands generated by the driver through the steering wheel and steering column, and convert them into electronic steering commands and transmit them to the electronic control unit.

[0108] The road sensor 2 is used to collect the current signal and rotation angle signal of the road sensor 3 and transmit them to the electronic control unit;

[0109] The road sensor motor driver 4 is used to receive control signals sent by the electronic control unit and control the road sensor motor 3 to generate simulated road feedback torque. The road feedback torque is fed back to the driver through the road sensor motor reducer 22, steering column 23 and steering wheel 1.

[0110] The electronic control unit 21 includes: a steering motor angle tracking controller A, a steering motor angle tracking controller B, a virtual spindle synchronization controller, a mean deviation coupling synchronization controller, and a road feel motor controller.

[0111] Steering motor angle tracking controller A and steering motor angle tracking controller B are used to track the electronic steering commands sent by the steering wheel angle sensor and generate control signals for steering motor driver A and steering motor driver B in the steering actuator.

[0112] The virtual spindle synchronization controller is used to calculate the virtual torque signal and adjust the target angle signals of steering motor A and steering motor B in the steering actuator in real time according to the virtual torque signal to reduce the angle synchronization error between the two motors.

[0113] The mean deviation coupling synchronization controller is used to send current compensation control signals to the steering motor angle tracking controller A and the steering motor angle tracking controller B respectively based on the mean deviation of the steering angle of steering motor A and steering motor B, so as to eliminate the steering angle synchronization error of steering motor A and steering motor B.

[0114] The road sensor motor controller is used to generate control commands to control the road sensor motor driver to drive the road sensor motor 3, so as to realize the simulation of road feedback torque;

[0115] The steering actuator includes: a steering motor driver A8, a steering motor A5, a steering motor reducer A12, a pinion A10, a steering motor angle sensor A6, a steering motor current sensor A7, a steering motor driver B17, a steering motor B20, a steering motor reducer B15, a pinion B14, a steering motor angle sensor B18, a steering motor current sensor B19, a rack 13, a steering tie rod 9, a left front wheel 10, and a right front wheel 16.

[0116] Steering motor driver A8 and steering motor driver B17, respectively, complete the angle tracking control of steering motor A5 and steering motor B20 according to the control signals of steering motor angle tracking controller A and steering motor angle tracking controller B;

[0117] Steering motor A5 is connected to pinion A11 via steering motor reducer A12, and steering motor B20 is connected to pinion B14 via steering motor reducer B15. Pinion A11 and pinion B14 mesh with rack 13, which is connected to steering tie rod 9. Steering tie rod 9 is connected to the left front wheel 10 and the right front wheel 16 respectively. Steering motor A5 and steering motor B20 work together to drive the steering of the left and right front wheels.

[0118] Steering motor angle sensor A6 and steering motor current sensor A7 are installed on steering motor A5; steering motor angle sensor A6 is used to collect the steering motor angle signal of steering motor A5 and send it to steering motor angle tracking controller A; steering motor current sensor A7 is used to collect the steering motor A5 current signal and send it to steering motor angle tracking controller A.

[0119] Steering motor angle sensor B18 and steering motor current sensor B19 are mounted on steering motor B20; steering motor angle sensor B18 is used to collect the steering motor angle signal of steering motor B20 and send it to steering motor angle tracking controller B; steering motor current sensor B19 is used to collect the steering motor B20 current signal and send it to steering motor angle tracking controller B.

[0120] The virtual spindle synchronization controller includes a virtual spindle, which is used to provide the same target angle signal for steering motor A and steering motor B.

[0121] The virtual torque calculator A in the virtual spindle synchronous controller calculates the virtual torque A based on the angle signal collected by the motor angle sensor A and the target angle signal output by the virtual spindle; the virtual torque calculator B calculates the virtual torque B based on the angle signal collected by the motor angle sensor B and the target angle signal output by the virtual spindle.

[0122] The steering motor angle tracking controller A includes: an adaptive radial basis neural network A and a fast terminal sliding mode controller A; the adaptive radial basis neural network A estimates the nonlinear state function of the steering motor A in real time based on the angle signal collected by the motor angle sensor A, and adaptively adjusts the state equation of the steering motor A; the fast terminal sliding mode controller A calculates the current control signal for the steering motor driver A based on the error between the angle signal collected by the motor angle sensor A and the target signal, so as to control the steering motor A.

[0123] The steering motor angle tracking controller B includes: an adaptive radial basis function neural network B and a fast terminal sliding mode controller B. The adaptive radial basis function neural network B estimates the nonlinear state function of the steering motor B in real time based on the angle signal collected by the motor angle sensor B, and adaptively adjusts the state equation of the steering motor B. The fast terminal sliding mode controller B calculates the current control signal for the steering motor driver B based on the error between the angle signal collected by the motor angle sensor B and the target signal, so as to control the steering motor B.

[0124] Specifically, the steering motor A and steering motor B are the same model of motor.

[0125] Reference Figure 2 , Figure 3 As shown, the present invention provides a synchronous tracking control method for a dual-motor steer-by-wire system, based on the aforementioned system, comprising the following steps:

[0126] Step 1: Establish a model of the dual-motor steer-by-wire system, including: a virtual spindle model, a steering motor model, a rack and pinion model, and a nonlinear tire model; specifically including:

[0127] The virtual spindle model is as follows:

[0128] (1);

[0129] In the formula, The moment of inertia of the virtual principal axis; The rotation angle of the virtual spindle; The damping coefficient of the virtual spindle; The virtual torque is A; The virtual torque is B; The virtual torque output by the virtual spindle;

[0130] The steering motor model is as follows:

[0131] (2);

[0132] In the formula, Let i be the moment of inertia of the steering motor i; Let i be the mechanical angular velocity of the steering motor i; Let be the damping coefficient of steering motor i; The load torque of steering motor i; The electromagnetic torque of steering motor i; Let i be the number of pole pairs of the steering motor i; This is the current control signal for steering motor i; Let be the permanent magnet flux linkage of steering motor i; where i = 1, 2, corresponding to steering motor A and steering motor B respectively;

[0133] The gear and rack model is as follows:

[0134] (3);

[0135] In the formula, The mass of the rack; This represents the displacement of the rack; is the damping coefficient of the rack; The steering resistance acting on the rack by the front wheels; Coulomb friction; , These are the reduction ratios of steering motor reducer A and steering motor reducer B, respectively. , These are the load torques of steering motor A and steering motor B, respectively. , These are the shaft stiffnesses of steering motor A and steering motor B, respectively. , These are the mechanical rotation angles of steering motor A and steering motor B, respectively. , These are the radii of pinion A and pinion B, respectively.

[0136] The nonlinear tire model is as follows:

[0137] (4);

[0138] In the formula, The lateral force of each front tire; The lateral stiffness of each front wheel; The slip angle of each front tire; The slip ratio of each front wheel tire; It is a boundary function; The coefficient of friction utilization; The road surface adhesion coefficient; For each front tire; These are the coefficients of the tire dynamic parameter model; The longitudinal speed of each front tire; For the longitudinal stiffness of each front tire; The steering resistance exerted by each front tire on the rack; and These are the mechanical trail and pneumatic trail of each front wheel, respectively.

[0139] Step 2: Using the vehicle status information and steering wheel commands, and based on the vehicle stability control principle, calculate the target front wheel steering angle to maintain vehicle stability;

[0140] Step 3: Using the target front wheel steering angle, the virtual torque A of steering motor A, and the virtual torque B of steering motor B, based on the torque feedback control principle and the virtual spindle model, adjust the target steering angle signals of steering motor A and steering motor B in real time; specifically including:

[0141] Step 31: Calculate virtual torque A and virtual torque B, as shown in the following expressions:

[0142] (5);

[0143] In the formula, The virtual torque is A; The virtual torque is B; This is the virtual feedback stiffness coefficient; This is the virtual steering angle correction coefficient for steering motor A;

[0144] Step 32: Calculate the virtual torque of the virtual spindle, using the following formula:

[0145] (6);

[0146] In the formula, The virtual stiffness coefficient of the virtual principal axis; The rotation angle of the virtual spindle is calculated based on the overall reduction ratio of the steering system and the rotation angle of the target wheel.

[0147] Combining equations (1) and (6), the output of the virtual spindle synchronous controller is calculated as follows:

[0148] (7);

[0149] The virtual spindle controls the output angle of the virtual spindle based on the virtual torque A and virtual torque B output by the virtual torque calculator A and virtual torque calculator B. This angle serves as the target angle for the steering motors A and B, thereby reducing the synchronization error between the steering motors A and B from the perspective of tracking the target.

[0150] Step 4: Using the error signal between the motor angle signals collected by motor angle sensors A and B and the target angle signals of steering motors A and B, the nonlinear state functions of the state equations of steering motors A and B are estimated in real time based on an adaptive radial basis function neural network; specifically including:

[0151] Step 41: Establish the state equation of steering motor i based on equations (2) and (3), as follows:

[0152] (8);

[0153] In the formula, Let i be the nonlinear state function of the steering motor i. ; Let i be the state vector of the steering motor i. , For the steering motor i's angle tracking error, ,in The target steering angle signal for steering motor i. The steering angle sensor i collects the steering angle signal of the steering motor; This is the derivative of the steering angle tracking error of steering motor i; For the system control gain of steering motor i, ; This is the control current signal for steering motor i; For the load interference of steering motor i, , , where D is the upper limit of the load torque of steering motor A and steering motor B;

[0154] Step 42: Design the input and output of the adaptive radial basis function neural network i, as follows:

[0155] (9);

[0156] In the formula, and These are the center position and width of neuron j, respectively; This is the output of the Gaussian function; For ideal neural network weights; For the approximation error of the neural network, , This is the upper bound of the estimation error of the neural network; the adaptive radial basis neural network i is either adaptive radial basis neural network A or adaptive radial basis neural network B;

[0157] Step 43: Estimating the nonlinear state function of steering motor i using the adaptive radial basis function neural network i for:

[0158] (10);

[0159] In the formula, for The transpose of the matrix; For weight estimation of ideal neural network weights, a design based on the Lyapunov stability principle is proposed. , These are adaptive coefficients; For the sliding surface of the fast terminal sliding mode controller i, , For linear term gain coefficients, The gain coefficient for the nonlinear term. for The exponent of the power is q, where q is the numerator of the fractional power and p is the denominator of the fractional power. Both p and q are positive odd numbers, and p > q.

[0160] An adaptive radial basis neural network i is used to estimate the nonlinear state function, thus avoiding the problem of decreased tracking accuracy caused by model parameter perturbation of steering motor i under complex operating conditions.

[0161] Step 5: Based on the target steering angle signals of steering motor A and steering motor B in Step 3, the nonlinear state functions of the state equations of steering motor A and steering motor B in Step 4, and the motor steering angle signals collected by motor angle sensor A and motor angle sensor B, calculate the current control signals of steering motor A and steering motor B respectively; specifically including:

[0162] Step 51: Design the fast terminal sliding surface i of the fast terminal sliding controller i as follows:

[0163] (11);

[0164] In the formula, The gain coefficient for the linear term; q is the gain coefficient of the nonlinear term; q is the numerator of the fractional power, p is the denominator of the fractional power, both p and q are positive odd numbers, and p > q;

[0165] Step 52: Based on the nonlinear state function of the steering motor i estimated by the fast terminal sliding surface i and the adaptive radial basis neural network, calculate the final control current signal of the fast terminal sliding controller i. ,as follows:

[0166] (12);

[0167] In the formula, This is the sliding mode gain coefficient; This is the switching gain coefficient;

[0168] The fast terminal sliding mode controller i outputs the final control current signal to the steering motor i to ensure that the steering motor i can track the target angle signal under the perturbation of model parameters.

[0169] Step 6: Based on the motor angle signals collected by motor angle sensor A and motor angle sensor B, calculate the mean angle deviation of steering motor A and steering motor B. Then, based on the variable approach law sliding mode control principle and the steering motor model, calculate the current compensation control signal for steering motor A and steering motor B. Use the obtained current compensation control signals to eliminate the angle synchronization error between steering motor A and steering motor B; specifically including:

[0170] Step 61: Design the variable approach law sliding surface as follows:

[0171] (13);

[0172] In the formula, Let be the system state vector, and satisfy . ; and For control coefficients, , ; Let be the smoothing coefficients of the hyperbolic tangent function, satisfying ; For the power exponent, satisfying ; As the benchmark weighting coefficient, satisfying ; This is the state gain adjustment coefficient; It is a state index; The width parameter of the hyperbolic secant function;

[0173] Step 62: Calculate the mean deviation of the steering angle of steering motor i, as follows:

[0174] (14);

[0175] In the formula, and These are the mean deviations of the steering angles of steering motor A and steering motor B, respectively. The average steering angle of steering motor A and steering motor B;

[0176] Step 63: Design the integral sliding surface for:

[0177] (15);

[0178] In the formula, This is the integral gain coefficient;

[0179] Step 64: Based on the integral sliding surface, calculate the current compensation control signal for steering motor A and the current compensation control signal for steering motor B output by the mean deviation coupled synchronous controller as follows:

[0180] (16);

[0181] in, These are the current compensation control signals for steering motor A and steering motor B.

[0182] Step 7: Using the current control signals of steering motor A and steering motor B in step 5 and the current compensation control signals of steering motor A and steering motor B in step 6, drive steering motor A and steering motor B to track the target turning angle signal; steering motor A and steering motor B drive the rack to move through steering motor reducer A and pinion A and steering motor reducer B and pinion B respectively, and then control the left front wheel and right front wheel to complete the steering through the steering tie rod.

[0183] This invention has many specific applications. The above description is only a preferred embodiment of this invention. It should be noted that for those skilled in the art, several improvements can be made without departing from the principle of this invention, and these improvements should also be considered within the scope of protection of this invention.

Claims

1. A synchronous tracking control system for a dual-motor steer-by-wire system, characterized in that, include: Vehicle stability control unit, steering wheel assembly unit, electronic control unit and steering actuator; The vehicle stability control unit is used to collect the vehicle's status information and assist the driver in controlling the vehicle. The steering wheel assembly unit is used to generate steering commands and transmit the steering commands, along with the current and angle signals from the road sensor motor, to the electronic control unit to generate simulated road feedback torque to the driver. The electronic control unit is used to generate current control signals for the steering motor, adjust the target steering angle of the dual motors to reduce synchronization error, calculate current compensation signals to eliminate steering angle synchronization error, and realize simulated feedback of road surface torque. The steering actuator is used to drive the steering motor according to the steering motor current control signal to achieve wheel steering; and transmits the collected steering motor angle signal and current signal to the electronic control unit.

2. The synchronous tracking control system for the dual-motor steer-by-wire system according to claim 1, characterized in that, The vehicle stability control unit includes: a vehicle status information sensor and a vehicle stability controller; Vehicle status information sensors are used to collect the status information of the entire vehicle; Vehicle stability controller, used to assist the driver in controlling the vehicle; The steering wheel assembly unit includes: a steering wheel, a steering column, a steering wheel angle sensor, a road feel motor sensor, a road feel motor, a road feel motor reducer, and a road feel motor driver. The steering wheel angle sensor is used to collect steering commands generated by the driver through the steering wheel and steering column, and transmit them to the electronic control unit; The road sensor is used to collect the current and rotation angle signals of the road sensor motor and transmit them to the electronic control unit. The road feel motor driver is used to receive control signals sent by the electronic control unit and control the road feel motor to generate simulated road feedback torque. The road feedback torque is fed back to the driver through the road feel motor reducer, steering column and steering wheel. The electronic control unit includes: a steering motor angle tracking controller A, a steering motor angle tracking controller B, a virtual spindle synchronization controller, a mean deviation coupling synchronization controller, and a road feel motor controller. Steering motor angle tracking controller A and steering motor angle tracking controller B are used to track the electronic steering commands sent by the steering wheel angle sensor and generate control signals for steering motor driver A and steering motor driver B in the steering actuator. The virtual spindle synchronization controller is used to calculate the virtual torque signal and adjust the target angle signals of steering motor A and steering motor B in the steering actuator in real time according to the virtual torque signal to reduce the angle synchronization error between the two motors. The mean deviation coupling synchronization controller is used to send current compensation control signals to the steering motor angle tracking controller A and the steering motor angle tracking controller B respectively based on the mean deviation of the steering angle of steering motor A and steering motor B, so as to eliminate the steering angle synchronization error of steering motor A and steering motor B. The road sensor motor controller is used to generate control commands to control the road sensor motor driver to drive the road sensor motor, so as to simulate the road feedback torque; The steering actuator includes: a steering motor driver A, a steering motor A, a steering motor reducer A, a pinion A, a steering motor angle sensor A, a steering motor current sensor A, a steering motor driver B, a steering motor B, a steering motor reducer B, a pinion B, a steering motor angle sensor B, a steering motor current sensor B, a rack, a steering tie rod, a left front wheel, and a right front wheel. Steering motor driver A and steering motor driver B, respectively, complete the angle tracking control of steering motor A and steering motor B based on the control signals of steering motor angle tracking controller A and steering motor angle tracking controller B; Steering motor A is connected to pinion A via steering motor reducer A, and steering motor B is connected to pinion B via steering motor reducer B. Pinion A and pinion B mesh with rack, and rack is connected to steering tie rod. Steering tie rod is connected to the left front wheel and the right front wheel respectively. Steering motor A and steering motor B work together to drive the steering of the left front wheel and the right front wheel. Steering motor angle sensor A and steering motor current sensor A are installed on steering motor A; steering motor angle sensor A is used to collect the steering motor angle signal and send it to steering motor angle tracking controller A; steering motor current sensor A is used to collect the steering motor angle signal and send it to steering motor angle tracking controller A. Steering motor angle sensor B and steering motor current sensor B are installed on steering motor B; steering motor angle sensor B is used to collect the steering motor B's angle signal and send it to steering motor angle tracking controller B; steering motor current sensor B is used to collect the steering motor B's current signal and send it to steering motor angle tracking controller B.

3. The synchronous tracking control system for the dual-motor steer-by-wire system according to claim 2, characterized in that, The virtual spindle synchronization controller includes a virtual spindle, which is used to provide the same target angle signal to steering motor A and steering motor B. The virtual torque calculator A in the virtual spindle synchronous controller calculates the virtual torque A based on the angle signal collected by the motor angle sensor A and the target angle signal output by the virtual spindle; the virtual torque calculator B calculates the virtual torque B based on the angle signal collected by the motor angle sensor B and the target angle signal output by the virtual spindle.

4. The synchronous tracking control system for the dual-motor steer-by-wire system according to claim 3, characterized in that, The steering motor angle tracking controller A includes: an adaptive radial basis neural network A and a fast terminal sliding mode controller A; the adaptive radial basis neural network A estimates the nonlinear state function of the steering motor A in real time based on the angle signal collected by the motor angle sensor A, and adaptively adjusts the state equation of the steering motor A; the fast terminal sliding mode controller A calculates the current control signal for the steering motor driver A based on the error between the angle signal collected by the motor angle sensor A and the target signal, so as to control the steering motor A; The steering motor angle tracking controller B includes: an adaptive radial basis neural network B and a fast terminal sliding mode controller B; the adaptive radial basis neural network B estimates the nonlinear state function of the steering motor B in real time based on the angle signal collected by the motor angle sensor B, and adaptively adjusts the state equation of the steering motor B. The fast terminal sliding mode controller B calculates the current control signal for the steering motor driver B based on the error between the angle signal collected by the motor angle sensor B and the target signal, so as to control the steering motor B.

5. A synchronous tracking control method for a dual-motor steer-by-wire system, based on the system described in any one of claims 1-4, characterized in that, The method includes the following steps: Step 1: Establish a model of the dual-motor steer-by-wire system, including: virtual spindle model, steering motor model, gear and rack model, and nonlinear tire model; Step 2: Using the vehicle status information and steering wheel commands, and based on the vehicle stability control principle, calculate the target front wheel steering angle to maintain vehicle stability; Step 3: Using the target front wheel angle, the virtual torque A of steering motor A and the virtual torque B of steering motor B, based on the torque feedback control principle and the virtual spindle model, adjust the target angle signals of steering motor A and steering motor B in real time. Step 4: Using the error signal between the motor angle signals collected by motor angle sensor A and motor angle sensor B and the target angle signals of steering motor A and steering motor B, the nonlinear state functions of the state equations of steering motor A and steering motor B are estimated in real time based on an adaptive radial basis neural network. Step 5: Based on the target angle signals of steering motor A and steering motor B in Step 3, the nonlinear state functions of the state equations of steering motor A and steering motor B in Step 4, and the motor angle signals collected by motor angle sensor A and motor angle sensor B, calculate the current control signals of steering motor A and steering motor B respectively. Step 6: Based on the motor angle signals collected by motor angle sensor A and motor angle sensor B, calculate the mean angle deviation of steering motor A and steering motor B. Based on the variable approach law sliding mode control principle and steering motor model, calculate the current compensation control signal of steering motor A and steering motor B. Eliminate the angle synchronization error between steering motor A and steering motor B according to the obtained current compensation control signal. Step 7: Using the current control signals of steering motor A and steering motor B in step 5 and the current compensation control signals of steering motor A and steering motor B in step 6, drive steering motor A and steering motor B to track the target turning angle signal; steering motor A and steering motor B drive the rack to move through steering motor reducer A and pinion A and steering motor reducer B and pinion B respectively, and then control the left front wheel and right front wheel to complete the steering through the steering tie rod.

6. The synchronous tracking control method for a dual-motor steer-by-wire system according to claim 5, characterized in that, Step 1 specifically includes: The virtual spindle model is as follows: （1）； In the formula, The moment of inertia of the virtual principal axis; The rotation angle of the virtual spindle; The damping coefficient of the virtual spindle; The virtual torque is A; The virtual torque is B; The virtual torque output by the virtual spindle; The steering motor model is as follows: （2）； In the formula, Let i be the moment of inertia of the steering motor i; Let i be the mechanical angular velocity of the steering motor i; Let be the damping coefficient of steering motor i; The load torque of steering motor i; The electromagnetic torque of steering motor i; Let i be the number of pole pairs of the steering motor i; This is the current control signal for steering motor i; Let be the permanent magnet flux linkage of steering motor i; where i = 1,2, corresponding to steering motor A and steering motor B respectively; The gear and rack model is as follows: （3）； In the formula, The mass of the rack; This represents the displacement of the rack; is the damping coefficient of the rack; The steering resistance acting on the rack by the front wheels; Coulomb friction; , These are the reduction ratios of steering motor reducer A and steering motor reducer B, respectively. , These are the load torques of steering motor A and steering motor B, respectively. , These are the shaft stiffnesses of steering motor A and steering motor B, respectively. , These are the mechanical rotation angles of steering motor A and steering motor B, respectively. , These are the radii of pinion A and pinion B, respectively. The nonlinear tire model is as follows: （4）； In the formula, The lateral force of each front tire; The lateral stiffness of each front wheel; The slip angle of each front tire; The slip ratio of each front wheel tire; It is a boundary function; The coefficient of friction utilization; The road surface adhesion coefficient; For each front tire; These are the coefficients of the tire dynamic parameter model; The longitudinal speed of each front tire; For the longitudinal stiffness of each front tire; The steering resistance exerted by each front tire on the rack; and These are the mechanical trail and pneumatic trail of each front wheel, respectively.

7. The synchronous tracking control method for a dual-motor steer-by-wire system according to claim 6, characterized in that, Step 3 specifically includes: Step 31: Calculate virtual torque A and virtual torque B, as shown in the following expressions: （5）； In the formula, The virtual torque is A; The virtual torque is B; This is the virtual feedback stiffness coefficient; This is the virtual steering angle correction coefficient for steering motor A; Step 32: Calculate the virtual torque of the virtual spindle, using the following formula: （6）； In the formula, The virtual stiffness coefficient of the virtual principal axis; The rotation angle of the virtual spindle is calculated based on the overall reduction ratio of the steering system and the rotation angle of the target wheel. Combining equations (1) and (6), the output of the virtual spindle synchronous controller is calculated as follows: （7）； The virtual spindle controls the output angle of the virtual spindle based on the virtual torque A and virtual torque B output by the virtual torque calculator A and virtual torque calculator B. This angle serves as the target angle for the steering motors A and B, thereby reducing the synchronization error between the steering motors A and B from the perspective of tracking the target.

8. The synchronous tracking control method for a dual-motor steer-by-wire system according to claim 7, characterized in that, Step 4 specifically includes: Step 41: Establish the state equation of steering motor i based on equations (2) and (3), as follows: （8）； In the formula, Let i be the nonlinear state function of the steering motor i. ; Let i be the state vector of the steering motor i. , For the steering motor i's angle tracking error, ,in The target steering angle signal for steering motor i. The steering angle sensor i collects the steering angle signal of the steering motor; This is the derivative of the steering angle tracking error of steering motor i; For the system control gain of steering motor i, ; This is the control current signal for steering motor i; For the load interference of steering motor i, , , where D is the upper limit of the load torque of steering motor A and steering motor B; Step 42: Design the input and output of the adaptive radial basis function neural network i, as follows: （9）； In the formula, and These are the center position and width of neuron j, respectively; This is the output of the Gaussian function; For ideal neural network weights; For the approximation error of the neural network, , This is the upper bound of the neural network estimation error; the adaptive radial basis function neural network i is either adaptive radial basis function neural network A or adaptive radial basis function neural network B; Step 43: Estimating the nonlinear state function of steering motor i using the adaptive radial basis function neural network i for: （10）； In the formula, for The transpose of the matrix; For weight estimation of ideal neural network weights, a design based on the Lyapunov stability principle is proposed. , These are adaptive coefficients; For the sliding surface of the fast terminal sliding mode controller i, , For linear term gain coefficients, The gain coefficient for the nonlinear term. for The exponent of the power is q, where q is the numerator of the fractional power and p is the denominator of the fractional power. Both p and q are positive odd numbers, and p > q. An adaptive radial basis neural network i is used to estimate the nonlinear state function, thus avoiding the problem of decreased tracking accuracy caused by model parameter perturbation of steering motor i under complex operating conditions.

9. The synchronous tracking control method for a dual-motor steer-by-wire system according to claim 8, characterized in that, Step 5 specifically includes: Step 51: Design the fast terminal sliding surface i of the fast terminal sliding controller i; Step 52: Based on the nonlinear state function of the steering motor i estimated by the fast terminal sliding surface i and the adaptive radial basis neural network, calculate the final control current signal of the fast terminal sliding controller i; The fast terminal sliding mode controller i outputs the final control current signal to the steering motor i to ensure that the steering motor i can track the target angle signal under the perturbation of model parameters.

10. The synchronous tracking control method for a dual-motor steer-by-wire system according to claim 9, characterized in that, Step 6 specifically includes: Step 61: Design the variable approach law sliding surface; Step 62: Calculate the mean deviation of the steering angle of steering motor i; Step 63: Design the integral sliding surface; Step 64: Based on the integral sliding surface, calculate the current compensation control signal of steering motor A and the current compensation control signal of steering motor B output by the mean deviation coupling synchronous controller.

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