A fuzzy adaptive control method and control system of a steer-by-wire system

Abstract

The present disclosure provides a fuzzy adaptive control method and control system of a steer-by-wire system, and relates to the technical field of automobile steering control, comprising: obtaining the motor current initial value, rack force estimation value and system initial road feeling state of the steer-by-wire system, and constructing a steering torque prediction model; predicting the future torque output of the steer-by-wire system by using the steering torque prediction model based on the fuzzy adaptive control algorithm, obtaining the predicted torque value and the fuzzy control parameter set; compensating the influence of motor current hysteresis and mechanical transmission efficiency based on the adaptive road feeling planning control model, obtaining the optimized motor current prediction value and steering execution torque value; outputting the fuzzy adaptive road feeling planning control signal based on the optimized motor current prediction value and steering execution torque value; and updating the steer-by-wire torque output in real time by using the fuzzy adaptive road feeling planning control signal feedback, so as to realize real-time control of the vehicle road feeling.

Description

Technical Field

[0001] The present disclosure relates to the technical field of automobile steering control, and in particular to a fuzzy adaptive control method and control system for a steer-by-wire system. Background Art

[0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art.

[0003] With the rapid development of autonomous driving technology, the road-feel control performance of steer-by-wire systems, as a core component of vehicle intelligent control, has become increasingly important in driving experience and safety. However, existing road-feel planning methods still lack real-time performance and accuracy.

[0004] Existing steer-by-wire systems generally use motor current and rack dynamics models, with road feel planning performed by estimating rack force. Invention patent CN202410685888.0, titled "A steer-by-wire system and steering control method thereof," proposes a steering control method based on vehicle status and road information, relying on real-time road feel data acquired by sensors for feedback. Invention patent CN201610165597.4, titled "A steer-by-wire system and control method based on fuzzy control," employs fuzzy control theory to optimize system response and improve response speed.

[0005] However, the above existing methods still have the following problems:

[0006] First, while existing systems use motor current and rack dynamics models for road feel planning, they rely on real-time road feel data acquired by sensors. This real-time data-based road feel control strategy has inherent limitations: current changes exhibit hysteresis, and due to the transmission efficiency of mechanical systems such as the motor reducer and rack-and-pinion steering gear, road feel planning itself exhibits hysteresis and inaccuracies. The mechanical hysteresis in the current transmission process makes it difficult for existing methods to provide timely road feel feedback under complex road conditions. This hysteresis not only affects the driving experience but can also reduce the vehicle's handling stability and safety in emergency or complex road conditions. Especially on rough or slippery roads, drivers often struggle to obtain accurate road feel feedback in a timely manner, which in turn affects steering precision and prevents effective prediction of road feel.

[0007] Second, existing fuzzy control methods lack adaptive capabilities when faced with complex and changing road environments, and are unable to promptly and accurately resolve the feedback delay problem caused by current hysteresis, affecting the vehicle's driving stability and safety under complex road conditions. Summary of the Invention

[0008] In order to solve the above problems, the present disclosure proposes a fuzzy adaptive control method and control system for a wire-controlled steer-by-wire system. A dual-winding permanent magnet synchronous motor is used as the steering motor, and fuzzy adaptive road feel planning control is introduced. By real-time monitoring and prediction of the current, angle and torque signals of the steering motor, combined with factors such as vehicle speed and road adhesion coefficient, a torque prediction model is constructed to optimize the output of motor current and steering torque, achieve precise adaptive road feel adjustment, and ensure the response speed and accuracy of the system under complex road conditions.

[0009] According to some embodiments, the present disclosure adopts the following technical solutions:

[0010] A fuzzy adaptive control method for a steer-by-wire system, comprising:

[0011] Obtain the initial motor current value, rack force estimation value, and initial road feel state of the steer-by-wire system to build a steering torque prediction model.

[0012] The initial motor current value and rack force estimate are input into the steering torque prediction model. Combined with the vehicle speed, steering wheel angle, and road adhesion coefficient, a fuzzy adaptive control algorithm is used to predict the future torque output of the steer-by-wire system, resulting in a predicted torque value and a set of fuzzy control parameters.

[0013] Based on the predicted torque value and fuzzy control parameter set combined with the motor current variation trend, an adaptive road feel planning control model is established. This model compensates for the effects of motor current hysteresis and mechanical transmission efficiency, and adjusts the road feel feedback signal in real time to obtain the optimized motor current prediction value and steering execution torque value.

[0014] Based on the optimized motor current prediction value and steering execution torque value, the controller parameters of the steer-by-wire system are adjusted in real time, and the output is a fuzzy adaptive road feel planning control signal;

[0015] The fuzzy adaptive road feel planning control signal is fed back to the steer-by-wire system controller to update the steer-by-wire torque output in real time, completing real-time road feel planning and feedback control for complex road conditions and achieving real-time control of the vehicle's road feel.

[0016] According to some embodiments, the present disclosure adopts the following technical solutions:

[0017] A wire-controlled steering system includes a dual-winding permanent magnet synchronous actuator motor controller, a dual-winding permanent magnet synchronous actuator motor, a motor reducer, a rotation angle sensor, a REPS steering gear, an ECU, a road feel simulation unit, and a fuzzy adaptive control system. The output shaft of the dual-winding permanent magnet synchronous actuator motor is connected to the rotating shaft of the REPS steering gear through the motor reducer; the dual-winding permanent magnet synchronous actuator motor controller and the housing of the dual-winding permanent magnet synchronous actuator motor adopt an integrated design, the dual-winding permanent magnet synchronous actuator motor controller is electrically connected to the dual-winding permanent magnet synchronous actuator motor and the ECU, and the dual-winding permanent magnet synchronous actuator motor controller is responsible for the operation of the dual-winding permanent magnet synchronous actuator motor.

[0018] According to some embodiments, the present disclosure adopts the following technical solutions:

[0019] A fuzzy adaptive control system for a steer-by-wire system, comprising:

[0020] The data acquisition module is used to obtain the initial value of the motor current, the estimated value of the rack force, and the initial road feel of the system of the steer-by-wire system, and to build a steering torque prediction model;

[0021] The prediction module is used to input the initial value of the motor current and the estimated value of the rack force into the steering torque prediction model. Combined with the vehicle speed, steering wheel angle, and road adhesion coefficient, it uses a fuzzy adaptive control algorithm to predict the future torque output of the steer-by-wire system to obtain the predicted torque value and a set of fuzzy control parameters;

[0022] The optimization module is used to establish an adaptive road feel planning control model based on the predicted torque value and fuzzy control parameter set combined with the motor current change trend. This model compensates for the effects of motor current hysteresis and mechanical transmission efficiency, and adjusts the road feel feedback signal in real time to obtain the optimized motor current prediction value and steering execution torque value.

[0023] The control module is used to adjust the controller parameters of the steer-by-wire system in real time based on the optimized motor current prediction value and steering execution torque value, and output a fuzzy adaptive road feel planning control signal; the fuzzy adaptive road feel planning control signal is fed back to the steer-by-wire system controller, and the steer-by-wire steering torque output is updated in real time, completing real-time road feel planning and feedback control for complex road conditions, and realizing real-time control of the vehicle's road feel.

[0024] According to some embodiments, the present disclosure adopts the following technical solutions:

[0025] A computer program product includes a computer program, wherein when the computer program is executed by a processor, the fuzzy adaptive control method of a steer-by-wire system is implemented.

[0026] According to some embodiments, the present disclosure adopts the following technical solutions:

[0027] A non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the fuzzy adaptive control method of a wire-controlled steer system is implemented.

[0028] According to some embodiments, the present disclosure adopts the following technical solutions:

[0029] An electronic device includes: a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement the fuzzy adaptive control method of a wire-controlled steering system.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] The disclosed steer-by-wire system uses a dual-winding permanent magnet synchronous motor as the steering motor. The dual-winding design provides high redundancy. Even if a single winding fails, the system can still maintain basic steering functions, thereby improving the reliability and safety of the system. By adding a road feel simulation unit, including a steering wheel, an angle torque sensor, a CEPS steering gear, and a dual-winding permanent magnet synchronous motor, it can accurately simulate the actual road feedback during driving, improving the driver's control and road feel experience. The steering wheel's angle and torque signals are transmitted in real time by the angle torque sensor to the CEPS steering gear and the dual-winding permanent magnet synchronous motor. The CEPS steering gear combines the signals to simulate road feedback force, thereby improving driving comfort and safety.

[0032] The present invention discloses a fuzzy adaptive control method for a steer-by-wire system. The method introduces a fuzzy adaptive road feel planning control module. By monitoring and predicting the current, steering angle, and torque signals of the steering motor in real time, and combining factors such as vehicle speed and road adhesion coefficient, the method constructs a torque prediction model, optimizes the output of motor current and steering torque, and achieves precise adaptive road feel adjustment, ensuring the system's response speed and accuracy under complex road conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings, which constitute a part of the present disclosure, are used to provide a further understanding of the present disclosure. The exemplary embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation to the present disclosure.

[0034] Figure 1 This is a structural diagram of a dual-winding motor steer-by-wire system according to an embodiment of the present disclosure;

[0035] Figure 2 This is a flowchart of the fuzzy adaptive control method for the steer-by-wire system according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0036] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.

[0037] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present disclosure belongs.

[0038] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0039] Example 1

[0040] In one embodiment of the present disclosure, a fuzzy adaptive control method for a steer-by-wire system is provided, comprising the following steps:

[0041] Step 1: Obtain the initial motor current value, rack force estimation value, and initial road feel state of the steer-by-wire system to build a steering torque prediction model.

[0042] Step 2: Input the initial motor current and rack force estimate into the steering torque prediction model. Combined with vehicle speed, steering wheel angle, and road adhesion coefficient, the fuzzy adaptive control algorithm is used to predict the future torque output of the steer-by-wire system, resulting in a predicted torque value and a set of fuzzy control parameters.

[0043] Step 3: Based on the predicted torque value and fuzzy control parameter set combined with the motor current variation trend, an adaptive road feel planning control model is established. This model compensates for the effects of motor current hysteresis and mechanical transmission efficiency, and adjusts the road feel feedback signal in real time to obtain the optimized motor current prediction value and steering actuation torque value.

[0044] Step 4: Based on the optimized motor current prediction value and steering torque value, the controller parameters of the steer-by-wire system are adjusted in real time to output a fuzzy adaptive road feel planning control signal;

[0045] Step 5: Feedback the fuzzy adaptive road feel planning control signal to the steer-by-wire system controller, update the steer-by-wire torque output in real time, complete real-time road feel planning and feedback control for complex road conditions, and achieve real-time control of the vehicle's road feel.

[0046] As an embodiment, the specific implementation process of the fuzzy adaptive control method of a steer-by-wire system disclosed in the present invention is as follows:

[0047] Step 1: Build a road feel prediction model based on the road feel motor current and rack dynamics model. Collect the steering torque, motor current, and rack motion state of the steer-by-wire system in real time to obtain the initial motor current value, rack force estimate, and the system's initial road feel state.

[0048] Specifically, a road feel prediction model based on the road feel motor current and rack dynamics model is established by establishing a mathematical model of the dual-winding permanent magnet synchronous road feel motor in a coordinate system, as well as the pinion and rack dynamics equations. The torque sensor onboard the steer-by-wire system then collects the steering torque signal, the motor current sensor onboard collects the current of the dual-winding permanent magnet synchronous motor, and the rack motion sensor onboard collects the rack motion state. The collected data is sent to the ECU via the CAN bus, which then obtains the initial motor current value, the rack force estimate, and the system's initial road feel state, including:

[0049] Step 11) Establish a mathematical model of the dual-winding permanent magnet synchronous induction motor in the d, q, x, and y axis coordinate systems:

[0050]

[0051] Among them, u d ,u q ,u x ,u y are the voltages on the d, q, x, and y axes respectively; L d , L q are the inductances on the d and q axes respectively; R s is the stator resistance; L s is the stator leakage inductance; f is the permanent magnet flux; i d ,i q ,i x ,i y , are the currents on the d, q, x, and y axes respectively.

[0052] The mathematical model discrete expression of the dual-winding permanent magnet synchronous induction motor in the d, q, x, y axis coordinate system is expressed as:

[0053]

[0054] The current predictions for the d, q, x, and y axes at time h+1 are:

[0055]

[0056] Among them, T sis the sampling period; i d (h+1), i q (h+1), i x (h+1), i y (h+1) are the currents on the d, q, x, and y axes at the moment (h+1).

[0057] Furthermore, the electromagnetic torque equation of the dual-winding permanent magnet synchronous induction motor is:

[0058] T e =3n p i q ψ f =K t i q

[0059] Among them, T e is the electromagnetic torque of the road-sensing motor; n p is the pole pair number.

[0060] Furthermore, the dynamic model of the dual-winding permanent magnet synchronous induction motor is:

[0061]

[0062] Among them, T L is the load torque; ω m is the motor mechanical angular velocity; B m is the damping coefficient of the dual-winding permanent magnet synchronous induction motor, J m is the motor shaft moment of inertia.

[0063] As an embodiment, the mathematical model of the dual-winding permanent magnet synchronous actuator motor is consistent with that of the dual-winding permanent magnet synchronous induction motor.

[0064] Step 12) Establish the dynamic equations of the pinion and rack:

[0065]

[0066] Among them, m r is the rack mass; x r is the rack displacement; B r is the rack damping factor; F R is the rack force; T E The output torque of the permanent magnet synchronous steering motor; θ sg is the pinion angle; N is the reduction ratio of the reduction mechanism; r p is the radius of the pinion.

[0067] Furthermore, by equating the rack force to the steering column, we can obtain:

[0068]

[0069] Among them, T r is the tire aligning torque equivalent to the steering column.

[0070] Step 13) The torque sensor onboard the steer-by-wire system collects the steering torque signal of the steer-by-wire system, the motor current sensor onboard collects the current of the dual-winding permanent magnet synchronous motor, and the rack motion sensor onboard collects the rack motion state. The sensors send the above collected data to the ECU via the CAN bus. The ECU obtains the initial value of the motor current, the estimated value of the rack force, and the initial road feel state of the system.

[0071] Step 2: Input the initial motor current value and the rack force estimate into the steering torque prediction model. Combined with the vehicle speed, steering wheel angle, and road adhesion coefficient, the fuzzy adaptive control algorithm is used to predict the future torque output of the steer-by-wire system. The predicted torque value and fuzzy control parameter set are obtained, including:

[0072] A steering torque prediction model and a fuzzy adaptive control algorithm are constructed. The steering torque prediction model based on the fuzzy adaptive control algorithm takes as input the vehicle speed, steering wheel angle, initial value of motor current, estimated value of rack force, and road adhesion coefficient, and outputs the predicted torque value. The fuzzy control rule is that the smaller the vehicle speed, the greater the influence of vehicle speed on steering torque; the larger the steering wheel angle, the greater the steering wheel torque proportional coefficient; and the larger the road adhesion coefficient, the greater the road adhesion influence coefficient. The fuzzy reasoning unit uses the fuzzy rule of the minimum value method to determine the conclusion based on the value of the input and the corresponding membership degree, and obtains the fuzzy control parameter set. The specific process is as follows:

[0073] Step 21) Establish a steering torque prediction model. The input of the steering torque prediction model is the vehicle speed v at time h, the steering wheel angle θ s , motor q-axis current i q , rack force F r , road adhesion coefficient μ, the output is the steering torque T predicted at time h+1 st .

[0074] Furthermore, the torque T acting on the steering wheel angle sw for:

[0075] T sw =i·T manual =i·K θ ·θ s

[0076] Where i is the steering ratio; T manual is the steering wheel manual torque; K θ is the steering wheel torque proportionality factor.

[0077] Furthermore, a nonlinear function is established to represent the steering torque T affected by speed. dyn for:

[0078]

[0079] Among them, K v It is a constant related to vehicle speed, reflecting the effect of speed on steering torque; C v is a regulation constant to avoid the denominator being zero; v is the vehicle speed.

[0080] Furthermore, the steering torque T μ for:

[0081] T μ =K μ μ

[0082] Among them, K μ is the road adhesion influence coefficient; μ is the road adhesion coefficient.

[0083] Furthermore, the steering torque T rack for:

[0084] T rack =N·F r ·r p

[0085] Then, the electromagnetic torque of the motor T is calculated based on the road sense. e , the torque T due to the steering wheel angle sw , steering torque T due to speed dyn , steering torque T due to road adhesion coefficient μ , steering torque T due to rack force rack A steering torque model of the steering system is established, and the steering torque is regarded as the sum of the component torques:

[0086]

[0087] The steering torque is predicted by discretizing the input variables. The discretization expression of the input variables is as follows:

[0088]

[0089] The steering torque prediction model is further obtained:

[0090]

[0091] Step 22) Establish fuzzy adaptive control algorithm

[0092] The input of the fuzzy controller is vehicle speed, steering wheel angle and road adhesion coefficient, and the output is speed proportional coefficient Kv , steering wheel torque proportional coefficient K θ , road adhesion influence coefficient K μ The fuzzy set of vehicle speed v is The fuzzy set of steering wheel angle is The fuzzy set of road adhesion coefficient is

[0093] Among them, the fuzzy control rule is: the smaller the speed, the greater the K v The larger the steering wheel angle, the greater the K θ The larger the road adhesion coefficient, the greater the K μ The larger the value, the more accurate the fuzzy inference unit is. The fuzzy inference unit uses the fuzzy rule of the minimum value method to determine the conclusion based on the value of the input quantity and the corresponding membership degree. The defuzzification adopts the centroid method.

[0094] Step 23) The vehicle speed, steering wheel angle, motor q-axis current, rack force, and road adhesion coefficient collected by the sensor are input into the steering torque prediction model based on the fuzzy adaptive control algorithm to obtain the predicted torque value and the fuzzy control parameter set (speed proportional coefficient K v , steering wheel torque proportional coefficient K θ , road adhesion influence coefficient K μ gather).

[0095] Step 3: Based on the predicted torque value and the fuzzy control parameter set combined with the motor current change trend, an adaptive road feel planning control model is established to compensate for the influence of motor current hysteresis and mechanical transmission efficiency, and the road feel feedback signal is adjusted in real time to obtain the optimized motor current prediction value and steering execution torque value, including: constructing a current hysteresis model, constructing a feedforward controller and a feedback controller of the adaptive road feel planning control model based on the current hysteresis model, the feedforward controller is pre-adjusted based on the predicted steering torque and current trend, and the real-time road feel feedback signal is generated by combining the feedback control and feedforward control outputs. The optimized motor current prediction value is obtained through the comprehensive adjustment of the real-time road feel feedback signal and the feedforward control, and the optimized steering execution torque is obtained based on the optimized motor current prediction value. The specific process is as follows:

[0096] Step 31) Establish a current hysteresis model:

[0097] i q (h)=i q (h-τ)+K d ·(T st (h)-T st (h-τ))

[0098] Where τ is the hysteresis time constant of the current response, K d is a gain factor related to the motor characteristics.

[0099] Furthermore, the output of the feedback controller can be expressed as:

[0100]

[0101] Where K f is the controller feedback gain.

[0102] Furthermore, the feedforward controller is pre-adjusted based on the predicted steering torque and current trends:

[0103]

[0104] Where, ΔT st (h) is the difference between the current steering torque and the predicted steering torque, K ff is the feedforward gain.

[0105] Furthermore, the feedback control and feedforward control outputs are combined to generate a real-time road feel feedback signal:

[0106]

[0107] The optimized motor current prediction value is obtained through the comprehensive adjustment of real-time road feedback signal and feedforward control.

[0108]

[0109] Where Δi q (h) is the compensation term for hysteresis and mechanical transmission efficiency.

[0110] Combined with the optimized motor current prediction value Get the optimized steering execution torque

[0111]

[0112] Step 4: Based on the optimized motor current prediction value and steering execution torque value, the controller parameters of the steer-by-wire system are adjusted in real time to output a fuzzy adaptive road feel planning control signal. This includes: adjusting the steer-by-wire system controller parameters in real time, setting the optimized motor current prediction value and steering execution torque value as the steer-by-wire control target, designing a steer-by-wire system controller based on feedback control, and sending the obtained control signal to the motor controller to adjust the motor current and steering execution torque in real time to obtain the final fuzzy adaptive road feel planning control signal. The specific process is as follows:

[0113] Step 41) Adjust the controller parameters of the steer-by-wire system in real time. Set the optimized motor current prediction value and steering execution torque value as the steer-by-wire control target.

[0114] Step 42) Design a steer-by-wire controller based on feedback control. The controller output U(h+1) is expressed as:

[0115]

[0116] Where e(h) is the difference between the optimized steering execution torque and the current steering torque.

[0117] Furthermore, the steer-by-wire controller sends the obtained control signal U(h) to the motor controller to adjust the motor current and steering torque in real time.

[0118]

[0119] The final fuzzy adaptive road sense planning control signal is U(h+1), i q (h+1).

[0120] Step 5: Feedback the fuzzy adaptive road feel planning control signal to the steer-by-wire system controller to update the steer-by-wire torque output in real time, completing real-time road feel planning and feedback control for complex road conditions, and achieving real-time control of the vehicle's road feel.

[0121] Specifically, the fuzzy adaptive road feel planning control signal obtained in step 4 is fed back to the steer-by-wire system controller to update the torque output of the steering actuator in real time, completing real-time road feel planning and feedback control for complex road conditions, achieving real-time and precise control of the vehicle's road feel, and ensuring the vehicle's handling stability and safety under complex and changeable road conditions.

[0122] Example 2

[0123] In one embodiment of the present disclosure, a wire-controlled steering system is provided, including a dual-winding permanent magnet synchronous executive motor controller, a dual-winding permanent magnet synchronous executive motor, a motor reducer, a rotation angle sensor, a REPS steering gear, an ECU, a road feel simulation unit and a fuzzy adaptive control system. The output shaft of the dual-winding permanent magnet synchronous executive motor is connected to the rotating shaft of the REPS steering gear through the motor reducer; the dual-winding permanent magnet synchronous executive motor controller and the housing of the dual-winding permanent magnet synchronous executive motor adopt an integrated design, the dual-winding permanent magnet synchronous executive motor controller is electrically connected to the dual-winding permanent magnet synchronous executive motor and the ECU, and the dual-winding permanent magnet synchronous executive motor controller is responsible for the operation of the dual-winding permanent magnet synchronous executive motor.

[0124] Furthermore, the motor reducer includes a worm gear reduction mechanism for converting the high speed and low torque of the dual-winding permanent magnet synchronous actuator motor into low speed and high torque, thereby driving the REPS steering gear.

[0125] Furthermore, the REPS steering gear is combined with the steering rack and related mechanisms to transmit the output torque of the dual-winding permanent magnet synchronous actuator motor to the vehicle's wheels to achieve the predetermined steering.

[0126] Furthermore, the ECU is electrically connected to the steering angle sensor and the motor controller, receives and processes the signals collected by the sensor, and sends commands to the dual-winding permanent magnet synchronous motor controller and the road feel simulation unit.

[0127] Furthermore, a steering angle sensor is installed at an appropriate position on the steering shaft to collect wheel steering angle data in real time and feed the information back to the dual-winding permanent magnet synchronous motor controller and ECU.

[0128] Furthermore, the rotation angle sensor is of patch type, with a simple structure and easy installation.

[0129] Furthermore, the road feel simulation unit includes a steering wheel, an angular torque sensor, a CEPS steering gear, a dual-winding permanent magnet synchronous road feel motor, and a dual-winding permanent magnet synchronous road feel motor controller.

[0130] Furthermore, the steering wheel is fixedly connected to one end of the steering column.

[0131] Furthermore, the angle and torque sensors are fixedly connected to the steering column to respectively collect the steering wheel angle and torque signals, and send the collected signals to the dual-winding permanent magnet synchronous road feel motor controller, ECU and fuzzy adaptive road feel planning control module;

[0132] Furthermore, the dual-winding permanent magnet synchronous road sense motor controller and the housing of the dual-winding permanent magnet synchronous road sense motor adopt an integrated design, the dual-winding permanent magnet synchronous road sense motor controller is electrically connected to the dual-winding permanent magnet synchronous road sense motor and the ECU, and the dual-winding permanent magnet synchronous road sense motor controller is responsible for the operation of the dual-winding permanent magnet synchronous executive motor.

[0133] The output shaft of the dual-winding permanent magnet synchronous road induction motor is connected to the CEPS steering gear.

[0134] The CEPS steering gear receives the torque signal from the steering angle torque sensor, simulates the road surface feedback force in real time, and outputs torque through the dual-winding permanent magnet synchronous road sensing motor, thereby simulating and transmitting the actual road surface feedback during driving.

[0135] Furthermore, the fuzzy adaptive road sense planning control system is electrically connected to the dual-winding permanent magnet synchronous road sense motor controller, the ECU, and the angular torque sensor.

[0136] Furthermore, the fuzzy adaptive road feel planning control system receives and processes the angle and torque signals collected by the angle torque sensor, and feeds back the calculation results to the dual-winding permanent magnet synchronous road feel motor controller.

[0137] Furthermore, the fuzzy adaptive road sense planning control system communicates with the steering motor controller via the CAN bus.

[0138] Example 3

[0139] In one embodiment of the present disclosure, a fuzzy adaptive control system for a steer-by-wire system is provided, comprising:

[0140] The data acquisition module is used to obtain the initial value of the motor current, the estimated value of the rack force, and the initial road feel of the system of the steer-by-wire system, and to build a steering torque prediction model;

[0141] The prediction module is used to input the initial value of the motor current and the estimated value of the rack force into the steering torque prediction model. Combined with the vehicle speed, steering wheel angle, and road adhesion coefficient, it uses a fuzzy adaptive control algorithm to predict the future torque output of the steer-by-wire system to obtain the predicted torque value and a set of fuzzy control parameters;

[0142] The optimization module is used to establish an adaptive road feel planning control model based on the predicted torque value and fuzzy control parameter set combined with the motor current change trend. This model compensates for the effects of motor current hysteresis and mechanical transmission efficiency, and adjusts the road feel feedback signal in real time to obtain the optimized motor current prediction value and steering execution torque value.

[0143] The control module is used to adjust the controller parameters of the steer-by-wire system in real time based on the optimized motor current prediction value and steering execution torque value, and output a fuzzy adaptive road feel planning control signal; the fuzzy adaptive road feel planning control signal is fed back to the steer-by-wire system controller, and the steer-by-wire steering torque output is updated in real time, completing real-time road feel planning and feedback control for complex road conditions, and realizing real-time control of the vehicle's road feel.

[0144] Example 4

[0145] In one embodiment of the present disclosure, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the fuzzy adaptive control method of a steer-by-wire system is implemented.

[0146] Example 5

[0147] In one embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the fuzzy adaptive control method of a wire-controlled steer system is implemented.

[0148] Example 6

[0149] In one embodiment of the present disclosure, an electronic device is provided, comprising: a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the fuzzy adaptive control method for a wire-controlled steering system.

[0150] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0151] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0152] Although the above describes the specific implementation methods of the present disclosure in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present disclosure. Those skilled in the art should understand that on the basis of the technical solution of the present disclosure, various modifications or variations that can be made by those skilled in the art without creative work are still within the scope of protection of the present disclosure.

Claims

1. A fuzzy adaptive control method for a steer-by-wire system, characterized in that: include: Obtain the initial motor current value, rack force estimation value, and initial road feel state of the steer-by-wire system to build a steering torque prediction model. The initial motor current value and rack force estimate are input into the steering torque prediction model. Combined with the vehicle speed, steering wheel angle, and road adhesion coefficient, a fuzzy adaptive control algorithm is used to predict the future torque output of the steer-by-wire system, resulting in a predicted torque value and a set of fuzzy control parameters. A steering torque prediction model and a fuzzy adaptive control algorithm are constructed. The steering torque prediction model based on the fuzzy adaptive control algorithm takes vehicle speed, steering wheel angle, initial motor current, rack force estimate, and road adhesion coefficient as inputs, and outputs a predicted torque value. The fuzzy control rule states that the lower the vehicle speed, the greater its influence on the steering torque; the greater the steering wheel angle, the greater the steering wheel torque proportional coefficient; and the greater the road adhesion coefficient, the greater the road adhesion influence coefficient. The fuzzy inference unit uses the minimum value method to determine the conclusion based on the input values ​​and the corresponding membership degrees, thereby obtaining a set of fuzzy control parameters. Based on the predicted torque value and fuzzy control parameter set combined with the motor current variation trend, an adaptive road feel planning control model is established. This model compensates for the effects of motor current hysteresis and mechanical transmission efficiency, and adjusts the road feel feedback signal in real time to obtain the optimized motor current prediction value and steering execution torque value. A current hysteresis model is constructed, and based on this model, a feedforward controller and feedback controller are constructed for the adaptive road feel planning control model. The feedforward controller performs pre-adjustments based on the predicted steering torque and current trend. The feedback control and feedforward control outputs are combined to generate a real-time road feel feedback signal. Through the combined adjustment of the real-time road feel feedback signal and the feedforward control, an optimized motor current prediction value is obtained. Based on this optimized motor current prediction value, an optimized steering actuation torque is obtained. Based on the optimized motor current prediction value and steering execution torque value, the controller parameters of the steer-by-wire system are adjusted in real time, and the output is a fuzzy adaptive road feel planning control signal; The fuzzy adaptive road feel planning control signal is fed back to the steer-by-wire system controller to update the steer-by-wire torque output in real time, completing real-time road feel planning and feedback control for complex road conditions and achieving real-time control of the vehicle's road feel.

2. The fuzzy adaptive control method for a steer-by-wire system according to claim 1, wherein: A mathematical model of the dual-winding permanent magnet synchronous road feel motor and the dynamic equations of the pinion and rack are established in the coordinate system. The torque sensor equipped with the steer-by-wire system is used to collect the steering torque signal of the steer-by-wire system. The motor current sensor is used to collect the current of the dual-winding permanent magnet synchronous motor. The rack motion sensor is used to collect the rack motion state. The collected data is sent to the ECU via the CAN bus. The ECU obtains the initial value of the motor current, the estimated value of the rack force and the initial road feel state of the system.

3. The fuzzy adaptive control method for a steer-by-wire system according to claim 1, wherein: The parameters of the steer-by-wire system controller are adjusted in real time, and the optimized motor current prediction value and steering execution torque value are set as the steer-by-wire control target. A steer-by-wire system controller based on feedback control is designed. The steer-by-wire system controller sends the obtained control signal to the motor controller, adjusts the motor current and steering execution torque in real time, and obtains the final fuzzy adaptive road feel planning control signal.

4. A steer-by-wire system, characterized in that: The fuzzy adaptive control method of a wire-controlled steering system specifically implemented as described in any one of claims 1-3 includes a dual-winding permanent magnet synchronous executive motor controller, a dual-winding permanent magnet synchronous executive motor, a motor reducer, an angle sensor, a REPS steering gear, an ECU, a road feel simulation unit and a fuzzy adaptive control system, wherein the output shaft of the dual-winding permanent magnet synchronous executive motor is connected to the rotating shaft of the REPS steering gear through the motor reducer; the dual-winding permanent magnet synchronous executive motor controller and the housing of the dual-winding permanent magnet synchronous executive motor adopt an integrated design, the dual-winding permanent magnet synchronous executive motor controller is electrically connected to the dual-winding permanent magnet synchronous executive motor and the ECU, and the dual-winding permanent magnet synchronous executive motor controller is responsible for the operation of the dual-winding permanent magnet synchronous executive motor.

5. A fuzzy adaptive control system for a steer-by-wire system, characterized in that: include: a data acquisition module for acquiring an initial motor current value, an estimated rack force value, and an initial road feel state of the system of the steer-by-wire system according to claim 4, and constructing a steering torque prediction model; The prediction module is used to input the initial value of the motor current and the estimated value of the rack force into the steering torque prediction model. Combined with the vehicle speed, steering wheel angle, and road adhesion coefficient, it uses a fuzzy adaptive control algorithm to predict the future torque output of the steer-by-wire system to obtain the predicted torque value and a set of fuzzy control parameters; The optimization module is used to establish an adaptive road feel planning control model based on the predicted torque value and fuzzy control parameter set combined with the motor current change trend. This model compensates for the effects of motor current hysteresis and mechanical transmission efficiency, and adjusts the road feel feedback signal in real time to obtain the optimized motor current prediction value and steering execution torque value. A control module, which adjusts the controller parameters of the steer-by-wire system in real time based on the optimized motor current prediction value and steering actuation torque value, and outputs a fuzzy adaptive road feel planning control signal; The fuzzy adaptive road feel planning control signal is fed back to the steer-by-wire system controller to update the steer-by-wire torque output in real time, completing real-time road feel planning and feedback control for complex road conditions and achieving real-time control of the vehicle's road feel.

6. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the fuzzy adaptive control method for a steer-by-wire system according to any one of claims 1 to 3 is implemented.

7. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by the processor, the fuzzy adaptive control method of the wire-controlled steering system according to any one of claims 1 to 3 is implemented.

8. An electronic device, characterized in that: include: A processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement a fuzzy adaptive control method for a wire-controlled steering system as described in any one of claims 1 to 3.

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