Fuzzy adaptive control method and control system for steer-by-wire system

Abstract

The invention provides a fuzzy adaptive control method and system for a steer-by-wire system, and relates to the technical field of automobile steering control, and the method comprises the steps: obtaining a motor current initial value, a rack force estimation value and a system initial road feeling state of the steer-by-wire system, and constructing a steering torque prediction model; predicting future torque output of the steer-by-wire system by using the steering torque prediction model based on the fuzzy adaptive control algorithm to obtain a predicted torque value and a fuzzy control parameter set; on the basis of a self-adaptive road feeling planning control model, the influence of motor current hysteresis and mechanical transmission efficiency is compensated, and an optimized motor current predicted value and a steering execution torque value are obtained; on the basis of the optimized motor current prediction value and the steering execution torque value, a fuzzy self-adaptive road feeling planning control signal is output and obtained; and the output of the steer-by-wire torque is updated in real time by using the fuzzy self-adaptive road feeling planning control signal feedback, so that the real-time control of the road feeling of the vehicle is realized.

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 a control system for a wire-controlled steering 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 sense control performance of the steer-by-wire system, as the core component of vehicle intelligent control, has become increasingly important in driving experience and driving safety. However, the existing road sense planning methods still have shortcomings in real-time and accuracy.

[0004] Existing wire-controlled steering systems generally use motor current and rack dynamics models to plan road feel through rack force estimation. Invention patent CN202410685888.0, entitled "A wire-controlled steering system and steering control method thereof", proposes a steering control method based on vehicle status and road information, relying on road feel data acquired in real time by sensors for feedback; invention patent CN201610165597.4, entitled "A wire-controlled steering system and control method thereof based on fuzzy control", uses fuzzy control theory to optimize system response and improve response speed.

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

[0006] First, although the existing system performs road feel planning based on motor current and rack dynamics models, it relies on road feel data obtained by sensors in real time. This road feel control strategy based on real-time data has inherent limitations: there is a lag in current changes, and due to the transmission efficiency of mechanical systems such as motor reducers and gear rack steering gears, the road feel planning itself also exhibits lag and inaccuracy; the mechanical lag in the current transmission process makes it difficult for existing methods to provide timely road feel feedback under complex road conditions. This lag not only affects the driving experience, but may also reduce the vehicle's handling stability and safety when dealing with emergency or complex road conditions. Especially on rugged or slippery road conditions, drivers often find it difficult to obtain accurate road feel feedback in a timely manner, which in turn affects the accuracy of steering operations and makes it impossible to effectively predict road feel.

[0007] Second, the existing fuzzy control methods lack adaptive capabilities when faced with complex and changing road environments, and it is difficult to promptly and accurately solve the feedback delay problem caused by current hysteresis, which affects the driving stability and safety of the vehicle under complex road conditions. Summary of the invention

[0008] In order to solve the above problems, the present invention proposes a fuzzy adaptive control method and control system for a wire-controlled steer system, adopts a dual-winding permanent magnet synchronous motor as a steering motor, introduces fuzzy adaptive road feel planning control, and constructs a torque prediction model by real-time monitoring and prediction of the current, steering angle and torque signals of the steering motor in combination with factors such as vehicle speed and road adhesion coefficient. The output of motor current and steering torque is optimized to achieve precise adaptive road feel adjustment, thereby ensuring 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 value of the motor current, the estimated value of the rack force, and the initial road feel state of the steer-by-wire system, and build a steering torque prediction model;

[0012] The initial value of the motor current and the estimated value of the rack force are input into the steering torque prediction model. The future torque output of the steer-by-wire system is predicted using the fuzzy adaptive control algorithm in combination with the vehicle speed, steering wheel angle and road adhesion coefficient to obtain the predicted torque value and fuzzy control parameter set.

[0013] Based on the predicted torque value and fuzzy control parameter set combined with the motor current change trend, an adaptive road sense planning control model is established to compensate for the influence of motor current hysteresis and mechanical transmission efficiency, and the road sense feedback signal is adjusted 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 wire-controlled steering system are adjusted in real time, and the fuzzy adaptive road sense planning control signal is output;

[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, an angle sensor, a REPS steering gear, an ECU, a road sense 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 state of the steer-by-wire system, and to build a steering torque prediction model;

[0021] A 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, and use the fuzzy adaptive control algorithm to predict the future torque output of the steer-by-wire system in combination with the vehicle speed, steering wheel angle and road adhesion coefficient, and obtain the predicted torque value and fuzzy control parameter set;

[0022] The optimization module is used to establish an adaptive road sense planning control model based on the predicted torque value and fuzzy control parameter set combined with the motor current change trend, compensate for the influence of motor current hysteresis and mechanical transmission efficiency, adjust the road sense feedback signal in real time, and 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 wire-controlled steering system in real time based on the optimized motor current prediction value and steering execution torque value, and output a fuzzy adaptive road sense planning control signal; the fuzzy adaptive road sense planning control signal is fed back to the wire-controlled steering system controller, and the wire-controlled steering torque output is updated in real time, so as to complete the real-time road sense planning and feedback control for complex road conditions and realize the real-time control of the vehicle road sense.

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

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

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

[0029] An electronic device comprises: 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 so that the electronic device executes the fuzzy adaptive control method for a wire-controlled steering system.

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

[0031] A wire-controlled steering system disclosed in the present invention adopts a dual-winding permanent magnet synchronous motor as a 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 is possible to accurately simulate the actual road feedback during driving, thereby improving the driver's sense of control and road feel. 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 the road feedback force, thereby improving driving comfort and safety.

[0032] A fuzzy adaptive control method for a wire-controlled steer system disclosed in the present invention introduces a fuzzy adaptive road feel planning control module, which constructs a torque prediction model by real-time monitoring and prediction of the current, steering angle and torque signals of the steering motor in combination with factors such as vehicle speed and road adhesion coefficient, optimizes the output of motor current and steering torque, realizes precise adaptive road feel adjustment, and ensures the response speed and accuracy of the system under complex road conditions. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0034] Figure 1 A structural diagram of a dual-winding motor wire-controlled steering system according to an embodiment of the present disclosure;

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

[0036] The present disclosure is further described below in conjunction with the accompanying drawings and embodiments.

[0037] It should be noted that the following detailed descriptions are all illustrative and are intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those 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 also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates 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 value of the motor current, the estimated value of the rack force, and the initial road feel state of the system of the steer-by-wire system, and build a steering torque prediction model;

[0042] Step 2: Input the initial value of the motor current and the estimated value of the rack force into the steering torque prediction model, and use the fuzzy adaptive control algorithm to predict the future torque output of the steer-by-wire system in combination with the vehicle speed, steering wheel angle, and road adhesion coefficient, and obtain the predicted torque value and fuzzy control parameter set;

[0043] Step 3: Based on the predicted torque value and fuzzy control parameter set combined with the motor current change trend, an adaptive road sense planning control model is established to compensate for the influence of motor current hysteresis and mechanical transmission efficiency, and the road sense feedback signal is adjusted in real time to obtain the optimized motor current prediction value and steering execution torque value;

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

[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 the real-time road feel planning and feedback control for complex road conditions, and realize 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 wire-controlled steering system disclosed in the present invention is as follows:

[0047] Step 1: Establish a road sense prediction model based on the road sense 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, and obtain the initial value of the motor current, the estimated value of the rack force and the initial road sense state of the system;

[0048] Specifically, the road sense prediction model based on the road sense motor current and rack dynamics model is established by establishing the mathematical model of the dual-winding permanent magnet synchronous road sense motor in the coordinate system and the pinion and rack dynamics equations. Then, the torque sensor carried by the wire-controlled steering system is used to collect the steering torque signal of the wire-controlled steering system, the motor current sensor carried by the motor collects the current of the dual-winding permanent magnet synchronous motor, and the rack motion sensor carried by the rack collects the rack motion state. The collected data is sent to the ECU through the CAN bus, and the ECU obtains the initial value of the motor current, the rack force estimation value and the initial road sense state of the system, including:

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

[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; ψ 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 of 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 time (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 the mathematical model of the dual-winding permanent magnet synchronous road 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 carried by the steer-by-wire system collects the steering torque signal of the steer-by-wire system, the motor current sensor carried by the system collects the current of the dual-winding permanent magnet synchronous motor, and the rack motion sensor carried by the system collects the rack motion state. The sensor sends the above collected data to the ECU via the CAN bus, and the ECU obtains the initial value of the motor current, the rack force estimation value and the initial road feel state of the system.

[0071] Step 2: 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, 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 input of the steering torque prediction model of the fuzzy adaptive control algorithm is the vehicle speed, steering wheel angle, motor current initial value, rack force estimation value and road adhesion coefficient, and the output is the predicted torque value; the fuzzy control rule is that the smaller the vehicle speed, the greater the influence of the vehicle speed on the steering torque; the larger the steering wheel angle, the greater the steering wheel torque proportional coefficient; the larger the road adhesion coefficient, the greater the road adhesion influence coefficient. The fuzzy reasoning unit determines the conclusion based on the fuzzy rule of the minimum value method according to the value of the input quantity 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, 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 is a constant related to vehicle speed, reflecting the effect of speed on steering torque; C v is the adjustment constant to avoid the denominator being zero; v is the vehicle speed.

[0080] Furthermore, the steering torque T due to the road adhesion coefficient 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 comprehensive road sense motor electromagnetic torque T 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 The steering torque model of the steering system is established, and the steering torque is regarded as the sum of each component torque:

[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] 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 greater the value. The fuzzy reasoning 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 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 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] In the formula, K f is the controller feedback gain.

[0102] Furthermore, the feedforward controller makes pre-adjustments 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 feed-forward gain.

[0105] Furthermore, the feedback control and feedforward control outputs are combined to generate a real-time road sense 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] In the formula, 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 wire steering system are adjusted in real time, and the fuzzy adaptive road sense planning control signal is output, including: adjusting the controller parameters of the wire steering system in real time, setting the optimized motor current prediction value and steering execution torque value as the wire steering control target, designing the wire steering system controller based on feedback control, the wire steering 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 sense planning control signal. The specific process is as follows:

[0113] Step 41) adjusting the controller parameters of the steer-by-wire system in real time, and setting 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, and 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 wire-controlled steering controller sends the obtained control signal U(h) to the motor controller to adjust the motor current and steering execution 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, update the steer-by-wire torque output in real time, complete the real-time road feel planning and feedback control for complex road conditions, and realize 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 wire-controlled steering system controller to update the torque output of the steering actuator in real time, complete the real-time road feel planning and feedback control of complex road conditions, achieve real-time and precise control of the vehicle's road feel, and ensure 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 actuator motor controller, a dual-winding permanent magnet synchronous actuator motor, a motor reducer, an 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.

[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 wheels of the vehicle 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 actuator motor controller and the road sense simulation unit.

[0127] Furthermore, a steering angle sensor is installed at an appropriate position of the steering shaft to collect wheel steering angle data in real time and feed the information back to the dual-winding permanent magnet synchronous actuator 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 angle torque sensor, a CEPS steering gear and 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 angle and torque signals of the steering wheel, and send the collected signals to the dual-winding permanent magnet synchronous road sense motor controller, ECU and fuzzy adaptive road sense 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 ECU, and the dual-winding permanent magnet synchronous road sense motor controller is responsible for the operation of the dual-winding permanent magnet synchronous actuator 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 the 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 sense 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 sense 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 state of the steer-by-wire system, and to build a steering torque prediction model;

[0141] A 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, and use the fuzzy adaptive control algorithm to predict the future torque output of the steer-by-wire system in combination with the vehicle speed, steering wheel angle and road adhesion coefficient, and obtain the predicted torque value and fuzzy control parameter set;

[0142] The optimization module is used to establish an adaptive road sense planning control model based on the predicted torque value and fuzzy control parameter set combined with the motor current change trend, compensate for the influence of motor current hysteresis and mechanical transmission efficiency, adjust the road sense feedback signal in real time, and 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 wire-controlled steering system in real time based on the optimized motor current prediction value and steering execution torque value, and output a fuzzy adaptive road sense planning control signal; the fuzzy adaptive road sense planning control signal is fed back to the wire-controlled steering system controller, and the wire-controlled steering torque output is updated in real time, so as to complete the real-time road sense planning and feedback control for complex road conditions and realize the real-time control of the vehicle road sense.

[0144] Example 4

[0145] In one embodiment of the present disclosure, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the fuzzy adaptive control method of a wire-controlled steering 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 steering 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, 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, 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 operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified 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. Technical personnel in the relevant field 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 value of the motor current, the estimated value of the rack force, and the initial road feel state of the steer-by-wire system, and build a steering torque prediction model; The initial value of the motor current and the estimated value of the rack force are input into the steering torque prediction model. The future torque output of the steer-by-wire system is predicted using the fuzzy adaptive control algorithm in combination with the vehicle speed, steering wheel angle and road adhesion coefficient to obtain the predicted torque value and fuzzy control parameter set. Based on the predicted torque value and fuzzy control parameter set combined with the motor current change trend, an adaptive road sense planning control model is established to compensate for the influence of motor current hysteresis and mechanical transmission efficiency, and the road sense feedback signal is adjusted in real time to obtain the optimized motor current prediction value and steering execution torque value; Based on the optimized motor current prediction value and steering execution torque value, the controller parameters of the wire-controlled steering system are adjusted in real time, and the fuzzy adaptive road sense planning control signal is output; 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 of a steer-by-wire system according to claim 1, characterized in that: A mathematical model of the dual-winding permanent magnet synchronous road feeling motor in the coordinate system and the dynamic equations of the pinion and rack are established. The torque sensor carried by 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, and the rack motion sensor is used to collect the rack motion state. The collected data is sent to the ECU through the CAN bus. The ECU obtains the initial value of the motor current, the estimated value of the rack force and the initial road feeling state of the system.

3. The fuzzy adaptive control method of a steer-by-wire system according to claim 1, characterized in that: A steering torque prediction model and a fuzzy adaptive control algorithm are constructed. The inputs of the steering torque prediction model of the fuzzy adaptive control algorithm are vehicle speed, steering wheel angle, initial value of motor current, estimated value of rack force and road adhesion coefficient, and the output is the predicted torque value. Among them, the fuzzy control rule is that the smaller the vehicle speed, the greater the influence of the vehicle speed on the steering torque; the larger the steering wheel angle, the greater the steering wheel torque proportional coefficient; the larger the road adhesion coefficient, the greater the road adhesion influence coefficient. The fuzzy reasoning unit determines the conclusion using the fuzzy rule of the minimum method according to the value of the input quantity and the corresponding membership, and obtains the fuzzy control parameter set.

4. The fuzzy adaptive control method of a steer-by-wire system according to claim 1, characterized in that: Construct a current hysteresis model, and construct a feedforward controller and feedback controller of the adaptive road sense planning control model based on the current hysteresis model. The feedforward controller makes pre-adjustments based on the predicted steering torque and current trend, and generates a real-time road sense feedback signal 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 sense feedback signal and the feedforward control. Based on the optimized motor current prediction value, the optimized steering execution torque is obtained.

5. The fuzzy adaptive control method of a steer-by-wire system according to claim 1, characterized in that: The parameters of the steer-by-wire system controller are adjusted in real time, the optimized motor current prediction value and steering execution torque value are set as the steer-by-wire control target, and 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.

6. A steer-by-wire system, characterized in that: It 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 sense 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.

7. A fuzzy adaptive control system for a steer-by-wire system, characterized in that: include: 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 state of the steer-by-wire system, and to build a steering torque prediction model; A 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, and use the fuzzy adaptive control algorithm to predict the future torque output of the steer-by-wire system in combination with the vehicle speed, steering wheel angle and road adhesion coefficient, and obtain the predicted torque value and fuzzy control parameter set; The optimization module is used to establish an adaptive road sense planning control model based on the predicted torque value and fuzzy control parameter set combined with the motor current change trend, compensate for the influence of motor current hysteresis and mechanical transmission efficiency, adjust the road sense feedback signal in real time, and obtain the optimized motor current prediction value and steering execution torque value; A control module, used to adjust 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 sense 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.

8. 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 wire-controlled steering system according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, a fuzzy adaptive control method for a wire-controlled steering system as described in any one of claims 1-6 is implemented.

10. 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 so that the electronic device executes a fuzzy adaptive control method for a wire-controlled steering system as described in any one of claims 1-6.

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