A method for controlling an active front wheel steering of a vehicle

Abstract

The present application relates to the field of vehicle engineering, in particular to a kind of automobile drive-by-wire active front wheel steering control method, first utilize Unscented Kalman filter and Dugoff tire model real-time identification road adhesion coefficient, then based on variable transmission ratio and neutral steering characteristic calculation front wheel angle feedforward compensation quantity;Octopus optimization algorithm is used to the parameter of double sliding mode controller of yaw velocity and mass center side slip angle is optimized, and the control buffeting of improvement exponential approach law is weakened;Based on the analysis calculation of mass center side slip angle-mass center side slip angle change rate phase plane, normalized distance and stability margin are realized, the adaptive weight distribution of double sliding mode controller output compensation angle is realized, after the superposition of total compensation angle and feedforward ideal angle, finally, the total ideal front wheel angle is output by the PID closed-loop control of steering actuator.The present application can dynamically adapt complex road conditions, coordinate trajectory tracking accuracy and vehicle lateral stability, effectively improve the robustness and control safety of line control steering system.

Description

Technical Field

[0001] This invention relates to the field of vehicle engineering technology, and more specifically to a method for steer-by-wire active front wheel steering control of automobiles. Background Technology

[0002] With the centralized evolution of automotive electronic and electrical architecture and the rapid implementation of steer-by-wire technology, steer-by-wire systems, by eliminating the mechanical hard connection between the steering wheel and steering wheels, have become the core execution system of the next generation of intelligent vehicles. Active Front Steering (AFS) control technology is the core control algorithm of steer-by-wire systems. By dynamically adjusting the front wheel steering angle through the steer-by-wire actuator, it completely decouples the driver's steering input from the vehicle's front wheel steering angle output, breaking through the fixed transmission ratio limitations of traditional mechanical steering systems. This effectively optimizes the vehicle's trajectory tracking ability and driving stability under complex conditions such as high-speed driving, cornering, and low-adhesion road surfaces. However, existing active front steering control technologies under the steer-by-wire system architecture suffer from problems such as lack of road surface adhesion recognition and dynamic parameter adaptation, insufficient dynamic coordination mechanisms for yaw and sideslip, poor adaptability of sliding mode controller design, and insufficient multi-module collaboration. These issues prevent the full utilization of the advantages of the steer-by-wire architecture and make it difficult to meet the safety and precise control requirements under complex and extreme conditions.

[0003] Chinese invention patent application CN 120135272 A, entitled "Sliding Mode Fault-Tolerant Control Method, Terminal and Storage Medium for Automotive Steer-by-Wire System," discloses a sliding mode fault-tolerant control scheme for a steer-by-wire system. This scheme uses a sliding mode controller to track the actual front wheel steering angle against a reference angle and a sliding mode observer to achieve fault-tolerant control in case of sensor failure. However, this scheme only considers steering angle tracking accuracy as the sole control objective, failing to achieve coordinated control of yaw rate and center of gravity sideslip angle, and failing to achieve a multi-objective balance between trajectory tracking and driving stability through the dynamic adjustment capability of the steering angle in steer-by-wire system.

[0004] In the Chinese invention patent application CN 118991922 A entitled "An Improved Sliding Mode Extension Control Method for Active Front Wheel Steering Based on an Extended State Observer", an improved sliding mode extension control scheme for active front wheel steering is disclosed. It designs an integral exponential type fast terminal sliding mode controller based on a fast exponential approach rate to control the yaw rate to track the ideal yaw rate. However, the sliding mode controller uses fixed control parameters, which need to be determined by repeated manual adjustments. When the road conditions change, it cannot achieve adaptive adjustment of the parameters, resulting in poor adaptability and robustness. Summary of the Invention

[0005] The purpose of this invention is to provide a steer-by-wire active front wheel steering control method for automobiles, which aims to solve the problems of insufficient adaptability of current steer-by-wire active front wheel steering control systems under complex road conditions, prominent contradictions in the coordinated control of yaw rate and sideslip angle, and poor handling stability under extreme conditions. The invention aims to enable front wheel steering control to have real-time perception of road adhesion coefficient, dynamic coordination and optimization of yaw rate and sideslip angle, and both robustness and precision of steering control.

[0006] To achieve the above objectives, the present invention provides a method for controlling active front wheel steering of an automobile by steer-by-wire, comprising the following steps:

[0007] Step 1: Use the unscented Kalman filter algorithm combined with the Dugoff tire model to identify the road surface adhesion coefficient, providing basic road condition parameters for subsequent control strategies;

[0008] Step 2: Design the feedforward compensation angle for the front wheel steering angle based on the variable transmission ratio and neutral steering characteristics;

[0009] Step 3: Taking the yaw rate tracking error and the center of gravity sideslip angle suppression effect as optimization objectives, the Octopus optimization algorithm is used to optimize the core control parameters of the yaw rate sliding mode controller and the center of gravity sideslip angle sliding mode controller, so as to provide a stable closed-loop control input for subsequent weight allocation and steering execution.

[0010] Step 4: Based on the phase plane analysis results of the vehicle's center of gravity sideslip angle and the rate of change of the center of gravity sideslip angle, the weights of the yaw rate and the front wheel compensation angle output by the center of gravity sideslip angle sliding mode controller are adaptively allocated. The allocated total compensation angle is then superimposed with the ideal front wheel angle after feedforward compensation based on the driver's input of neutral steering characteristics, and the final output is the total ideal front wheel angle of the steer-by-wire system.

[0011] Step 5: Input the desired front wheel angle as the target value into the steer-by-wire system, and achieve rapid tracking of the front wheel angle through closed-loop control of the DC motor, thus completing the full-process closed-loop control of active front wheel steering.

[0012] Optionally, in step 1, the average of the road adhesion coefficients identified by the four tires can be taken as the current road adhesion coefficient value.

[0013] Optionally, the process of obtaining the front wheel compensation angle based on the yaw rate in step 3 includes the following steps:

[0014] The ideal yaw rate and ideal center-of-gravity sideslip angle are obtained from the average adhesion coefficient of the four tires' real-time road adhesion coefficients output in step 1. Calculated using an ideal two-degree-of-freedom vehicle model;

[0015] The two-degree-of-freedom model of the vehicle was modified to include a yaw rate differential equation with compensation for steering angle:

[0016]

[0017] The approach law for the yaw rate sliding mode controller adopts a modified exponential approach law:

[0018]

[0019] Substituting the derivative expression of the sliding surface, we obtain the front wheel compensation angle based on the yaw rate:

[0020]

[0021] in, yaw rate The first derivative, , , Sliding surface , For sliding surface The first derivative, the error between the actual yaw rate and the ideal yaw rate. , For the ideal yaw rate, , represents the yaw rate sliding surface parameters. The coefficient of the yaw rate approach law. This is the yaw rate chattering suppression coefficient. The centroid sideslip angle The first derivative, For ideal yaw rate The second derivative, Error between actual yaw rate and ideal yaw rate The first derivative, The ideal front wheel steering angle is compensated based on feedforward compensation of neutral steering characteristics. The first derivative, For longitudinal vehicle speed, Wheelbase The distance from the center of gravity to the front axle. The distance from the center of gravity to the rear axle. For front axle lateral stiffness, For rear axle lateral stiffness, This represents the moment of inertia of the entire vehicle about its vertical axis.

[0022] Optionally, the process of obtaining the front wheel compensation angle based on the center of gravity sideslip angle in step 3 includes the following steps:

[0023] The two-degree-of-freedom model of the vehicle was modified to include the differential equation for the center of mass sideslip angle with compensation for rotation:

[0024]

[0025] The approach law for the centroid sideslip angle sliding mode controller adopts a modified exponential approach law:

[0026]

[0027] Substituting the derivative expression of the sliding surface, we obtain the front wheel compensation angle based on the sideslip angle of the center of gravity:

[0028]

[0029] in, , , Sliding surface Error between actual centroid sideslip angle and ideal centroid sideslip angle , For the ideal centroid sideslip angle, , where is the sliding surface parameter for the centroid side slip angle. The coefficient of the centroid sideslip angle approach law. This is the flutter suppression coefficient for the center of mass sideslip angle. The centroid sideslip angle The first derivative, For the ideal centroid sideslip angle The second derivative, The error between the actual centroid sideslip angle and the ideal centroid sideslip angle first derivative The ideal front wheel steering angle is compensated based on feedforward compensation of neutral steering characteristics. The first derivative, For the overall vehicle weight.

[0030] Optionally, the octopus optimization algorithm parameter optimization step specifically involves optimizing the yaw rate and center-of-gravity sideslip angle parameters of the dual sliding mode controller using the octopus optimization algorithm. The goal is to minimize the root mean square error of the yaw rate tracking error and the root mean square error of the center-of-gravity sideslip angle suppression error, respectively, by optimizing the sliding surface parameters. , The coefficient of the approach law , Boom suppression coefficient , To find the best option.

[0031] Optionally, step 4 uses normalized distance and stability margin to calculate and optimize the adaptive weight allocation, including the following steps:

[0032] Step 4.1: At the centroid side slip angle With the rate of change of the centroid side deflection angle In the phase plane, the double-line method uses the saddle point, i.e. the unstable equilibrium point, of the vehicle's lateral dynamics system as the core reference to draw the critical boundary;

[0033] To achieve rapid solution of stability domain parameters under all working conditions, the front wheel steering angle, road adhesion coefficient, and vehicle speed are discretized and sampled to calculate the slope under each working condition. intercept with the lower boundary of the stability region intercept of the upper boundary of the stability region The baseline data is obtained by linear fitting of parameters under fixed vehicle speed and adhesion coefficient. Then, the parameter mapping relationship is established by two-dimensional linear interpolation to achieve accurate calculation of stability domain parameters across the entire operating condition range.

[0034] Step 4.2: Using the shortest distance formula from a point to a line, define the normalized distance from the vehicle's current state point to the stability boundary. The normalization range is set to [0,1];

[0035] Step 4.3: Based on the normalized distance It can make adaptive adjustments.

[0036] Optionally, the adaptive adjustment strategy in step 4.3 is as follows:

[0037] Normalized distance Stability margin index mapped to the [0,1] interval ;

[0038] Weights for yaw rate tracking are dynamically generated using the Sigmoid nonlinear function. :

[0039]

[0040] in, The weights for yaw rate tracking, The weights for suppressing centroid sideslip angle. , To control the switching rate, The threshold center;

[0041] when hour, Increase the yaw rate, and the controller will prioritize tracking the yaw rate;

[0042] when hour, The angle decreases rapidly, and the controller prioritizes suppressing the centroid sideslip angle.

[0043] Optionally, step 4 may also include the steps of weighted fusion of compensated rotation angles and output of total rotation angle:

[0044] The total compensation angle calculation is based on the adaptive weights obtained in step 4.3, which affect the compensation angle output by the dual sliding mode controller. and The total compensated turning angle is obtained by performing smooth weighted fusion. When the vehicle is in a high stability margin range, the total compensation angle is... Prioritize ensuring yaw rate tracking accuracy; when vehicle stability margin decreases, the total compensation angle is... The main function is to prioritize suppressing the center of gravity sideslip angle to ensure the vehicle's lateral stability. When the vehicle is at the critical point of instability or in an unstable state, the total compensation angle is output entirely by the center of gravity sideslip angle sliding mode controller, which forcibly pulls the vehicle back to the stable range.

[0045] The Prime Minister wants to include the total compensation angle from the total compensation angle calculation in the front wheel steering angle calculation. Ideal front wheel steering angle after feedforward compensation based on neutral steering characteristics, input by the driver. By superimposing these values, the total ideal front wheel steering angle output by the active front wheel steering system to the steering actuator is obtained. .

[0046] This invention provides a steer-by-wire active front wheel steering control method for automobiles. First, an unscented Kalman filter algorithm combined with a Dugoff tire model is used to identify the road adhesion coefficients of the four tires in real time and calculate their average values. Then, a speed-adaptive variable transmission ratio module and a feedforward compensation module based on neutral steering are designed to obtain the ideal front wheel steering angle for the vehicle's driving state. Next, an octopus optimization algorithm is used to independently optimize the core parameters of the dual sliding mode controller for yaw rate and center of gravity sideslip angle, aiming to minimize the tracking error of yaw rate and the suppression error of center of gravity sideslip angle, respectively. This constructs a dual sliding mode controller and outputs front wheel steering angle compensation values ​​separately. Then, a lateral stability boundary for the vehicle is constructed based on the phase plane of center of gravity sideslip angle and the rate of change of center of gravity sideslip angle. The normalized stability margin of the vehicle's current state is calculated, and an adaptive weight allocation of the dual sliding mode controller outputs is achieved through a Sigmoid nonlinear function. The total compensated steering angle is then obtained and superimposed on the ideal front wheel steering angle after feedforward compensation. Finally, precise tracking of the front wheel steering angle is achieved through PID closed-loop control of the steering actuator. This invention solves the technical problems of poor road surface adaptability, difficulty in dynamically balancing maneuverability and stability, and coupling interference of multiple control targets in traditional active front wheel steering control. It is particularly suitable for real-time active front wheel steering control of automotive steer-by-wire systems. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a schematic diagram of the active front wheel steering control strategy framework for vehicle steer-by-wire based on road surface recognition, as described in this invention.

[0049] Figure 2 This is a road surface adhesion coefficient diagram identified by different tires in a specific embodiment of the present invention.

[0050] Figure 3 This is a flowchart of phase plane stability domain analysis and adaptive weighted fusion control according to a specific embodiment of the present invention.

[0051] Figure 4 This is a phase plane diagram of a specific embodiment of the present invention with a turning angle of 0.01 rad, a vehicle speed of 60 km / h, and a road surface adhesion coefficient of 0.85.

[0052] Figure 5 This is a comparison chart of yaw rate with and without AFS control in a specific embodiment of the present invention.

[0053] Figure 6 This is a comparison diagram of the centroid side slip angle with and without AFS control in a specific embodiment of the present invention.

[0054] Figure 7 This is a comparison diagram of the front wheel steering angle with and without AFS control in a specific embodiment of the present invention. Detailed Implementation

[0055] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0056] This invention provides a method for controlling active front wheel steering of an automobile by steer-by-wire, comprising the following steps:

[0057] Step 1: Use the unscented Kalman filter algorithm combined with the Dugoff tire model to identify the road surface adhesion coefficient, providing basic road condition parameters for subsequent control strategies;

[0058] Step 2: Design the feedforward compensation angle for the front wheel steering angle based on the variable transmission ratio and neutral steering characteristics;

[0059] Step 3: Taking the yaw rate tracking error and the center of gravity sideslip angle suppression effect as optimization objectives, the Octopus optimization algorithm is used to optimize the core control parameters of the yaw rate sliding mode controller and the center of gravity sideslip angle sliding mode controller, so as to provide a stable closed-loop control input for subsequent weight allocation and steering execution.

[0060] Step 4: Based on the phase plane analysis results of the vehicle's center of gravity sideslip angle and the rate of change of the center of gravity sideslip angle, the weights of the yaw rate and the front wheel compensation angle output by the center of gravity sideslip angle sliding mode controller are adaptively allocated. The allocated total compensation angle is then superimposed with the ideal front wheel angle after feedforward compensation based on the driver's input of neutral steering characteristics, and the final output is the total ideal front wheel angle of the steer-by-wire system.

[0061] Step 5: Input the desired front wheel angle as the target value into the steer-by-wire system, and achieve rapid tracking of the front wheel angle through closed-loop control of the DC motor, thus completing the full-process closed-loop control of active front wheel steering.

[0062] In step 1 above, the average of the road adhesion coefficients identified by the four tires is taken as the current road adhesion coefficient value.

[0063] The specific process of step 3 is as follows:

[0064] Step 3.1: Design of yaw rate sliding mode controller.

[0065] The ideal yaw rate and ideal center of gravity sideslip angle are obtained based on the average adhesion coefficient obtained from the real-time road adhesion coefficients of the four tires output in step 1. The results were obtained from calculations using an ideal two-degree-of-freedom vehicle model.

[0066] The two-degree-of-freedom model of the vehicle was modified to include a yaw rate differential equation with compensation for steering angle:

[0067] The approach law for the yaw rate sliding mode controller adopts a modified exponential approach law:

[0068]

[0069] Substituting the derivative expression of the sliding surface, we obtain the front wheel compensation angle based on the yaw rate:

[0070]

[0071] Step 3.2, Design of the sliding mode controller for the centroid side slip angle.

[0072] The two-degree-of-freedom model of the vehicle was modified to include the differential equation for the center of mass sideslip angle with compensation for rotation:

[0073]

[0074] The approach law for the centroid sideslip angle sliding mode controller adopts a modified exponential approach law:

[0075]

[0076] Substituting the derivative expression of the sliding surface, we obtain the front wheel compensation angle based on the sideslip angle of the center of gravity:

[0077]

[0078] Step 3.3: Optimize the yaw rate and center of mass sideslip angle parameters of the dual sliding mode controller using the octopus optimization algorithm.

[0079] In the above formula, yaw rate The first derivative, , , Sliding surface , For sliding surface The first derivative, the error between the actual yaw rate and the ideal yaw rate. , For the ideal yaw rate, , where is the sliding surface parameter. For the reaching law coefficient, This is the chatter suppression coefficient. The centroid sideslip angle The first derivative, For ideal yaw rate The second derivative, Error between actual yaw rate and ideal yaw rate The first derivative, The ideal front wheel steering angle is compensated based on feedforward compensation of neutral steering characteristics. The first derivative, , , Sliding surface Error between actual centroid sideslip angle and ideal centroid sideslip angle , For the ideal centroid sideslip angle, , where is the sliding surface parameter. For the reaching law coefficient, This is the chatter suppression coefficient. For the ideal centroid sideslip angle The second derivative, Error between actual yaw rate and ideal yaw rate The first derivative, The ideal front wheel steering angle is compensated based on feedforward compensation of neutral steering characteristics. The first derivative, For longitudinal vehicle speed, Wheelbase For the overall vehicle quality, The distance from the center of gravity to the front axle. The distance from the center of gravity to the rear axle. For front axle lateral stiffness, For rear axle lateral stiffness, This represents the moment of inertia of the entire vehicle about its vertical axis.

[0080] Step 4 includes the following steps:

[0081] Step 4.1, at the centroid side deflection angle With the rate of change of the centroid side deflection angle In the phase plane, the double-line method uses the saddle point, i.e. the unstable equilibrium point, of the vehicle's lateral dynamics system as the core reference for drawing the critical boundary.

[0082] First, based on the two-degree-of-freedom vehicle model and the tire nonlinear adhesion limit, the coordinates of the saddle point in the phase plane representing the critical point of vehicle instability are obtained. , Then, using the saddle point as the key node, two straight boundary lines with different slopes are fitted. Both lines pass through or are constrained at the saddle point, dividing the phase plane into an inner stable domain and an outer unstable domain, thereby realizing the construction of the vehicle's lateral dynamic stability boundary based on the saddle point.

[0083] To achieve rapid solution of stability domain parameters under all working conditions, the front wheel steering angle, road adhesion coefficient, and vehicle speed are discretized and sampled to calculate the slope under each working condition. intercept with the lower boundary of the stability region intercept of the upper boundary of the stability region By linearly fitting the parameters at a fixed vehicle speed and adhesion coefficient to obtain baseline data, and then establishing parameter mapping relationships through two-dimensional linear interpolation, accurate calculation of stability domain parameters can be achieved across the entire operating condition range.

[0084] Step 4.2: Calculate the normalized distance.

[0085] Using the shortest distance formula from a point to a line, the normalized distance from the vehicle's current state point to the stability boundary is defined. The normalization range is set to [0,1]:

[0086] Normalized distance The closer the value is to 1, the closer the vehicle is to its instability limit. The closer the value is to 0, the more stable the vehicle is, and the higher its safety level. If the value is greater than 1, the vehicle will become unstable.

[0087] Step 4.3, Adaptive weight allocation.

[0088] Based on the normalized distance obtained in step 4.2 The control weights are adaptively adjusted.

[0089] when At this time, the vehicle is in a stable range and has high safety. The front wheel compensation angle is calculated by the optimized dual sliding mode controller.

[0090] when At this point, the vehicle's condition approaches or exceeds the stability boundary, and the vehicle is nearing the instability limit.

[0091] To achieve a smooth transition of the aforementioned control weights, the normalized distance is... Stability margin index mapped to the [0,1] interval .

[0092] when At that time (i.e.) (), indicating that the current vehicle status is the most stable.

[0093] when At that time (i.e.) This indicates that the vehicle is currently close to or in a state of instability.

[0094] Weights for yaw rate tracking are dynamically generated using the Sigmoid nonlinear function. :

[0095]

[0096] in, The weights for yaw rate tracking, The weights for suppressing centroid sideslip angle. , To control the switching rate, The threshold center is [the value of the threshold].

[0097] when hour, Increase the yaw rate, and the controller will prioritize tracking the yaw rate.

[0098] when hour, The angle decreases rapidly, and the controller prioritizes suppressing the centroid sideslip angle.

[0099] Step 4.4: Calculate the total compensation angle.

[0100] Based on the adaptive weights obtained in step 4.3, the compensation angle of the dual sliding mode controller output is adjusted. and The total compensated turning angle is obtained by performing smooth weighted fusion. .

[0101] When the vehicle is in a high stability margin range, the total compensation angle is... Prioritize ensuring the accuracy of yaw rate tracking.

[0102] When the vehicle stability margin decreases, the total compensation angle is... Prioritize suppressing the center of gravity sideslip angle to ensure the vehicle's lateral stability.

[0103] When the vehicle is at the critical point of instability or in an unstable state, the total compensation angle is output entirely by the center of gravity sideslip angle sliding mode controller, which forcibly pulls the vehicle back to the stable range, achieving a balance between maneuverability and stability under all operating conditions.

[0104] Step 4.5: The Prime Minister turns the front wheel to the side.

[0105] The total compensation angle obtained in step 4.4 Ideal front wheel steering angle after feedforward compensation based on neutral steering characteristics, input by the driver. By superimposing these values, the total ideal front wheel steering angle output by the active front wheel steering system to the steering actuator is obtained. .

[0106] For further details, please refer to Figures 1 to 7 The present invention will be further described in detail through specific embodiments:

[0107] In this embodiment, all data acquisition and discretization algorithm calculations use a fixed sampling step size, with a sampling frequency set to 10kHz, corresponding to a single sampling step size of 0.0001 seconds. Figure 1 The diagram illustrates the active front wheel steering strategy framework for vehicle steer-by-wire based on road surface recognition, as shown in this invention. The specific execution flow is as follows:

[0108] Step 1: Road surface adhesion coefficient identification. The road surface adhesion coefficient is identified using an unscented Kalman filter algorithm combined with a Dugoff tire model.

[0109] In this embodiment, a simulation test was conducted using a road surface with a coefficient of adhesion of 0.85. The vehicle speed was set to 60 km / h (16.7 m / s), and the vehicle driving process was a typical complex ISO double lane change standard condition. The total driving time of the condition was 15 seconds, the sampling frequency was 10 kHz, and a total of 150,000 sets of vehicle dynamics data points were collected. The road surface adhesion coefficient was identified using an unscented Kalman filter algorithm combined with the Dugoff tire model. The changes in the adhesion coefficients of the four tires were identified as follows: Figure 2 As shown.

[0110] The average road surface adhesion coefficient was obtained based on the four identified tire adhesion coefficients. This is used in subsequent control steps.

[0111]

[0112] Step 2: Design a feedforward compensation angle for the front wheel steering angle based on the variable transmission ratio and neutral steering characteristics. This provides a transmission ratio correction coefficient and a feedforward compensation angle for dual sliding mode control and weight allocation, thereby achieving preliminary and precise control of active front wheel steering.

[0113] Variable gear ratio refers to the ratio of steering wheel angle to steering wheel angle, which is dynamically adjusted according to vehicle speed.

[0114] When driving at low speeds, a smaller gear ratio is used, allowing the steering wheel to turn at a small angle to achieve a larger steering wheel angle, thus improving the convenience of parking and low-speed maneuvering.

[0115] When driving at high speeds, a larger gear ratio is used, requiring the steering wheel to be turned at a larger angle to drive the steering wheels to turn slightly. This improves the stability of the vehicle when driving straight and avoids loss of control due to oversteering at high speeds.

[0116] Feedforward compensation based on neutral steering characteristics is an advanced strategy in vehicle steering control. It directly calculates and applies the required front wheel angle for compensation based on reference inputs such as steering wheel angle and vehicle speed, as well as the vehicle dynamics model. This angle is then superimposed on the feedback front wheel angle to eliminate steering lag and quickly bring the vehicle closer to a neutral steering state.

[0117] The specific implementation steps are as follows:

[0118] Step 2.1, Design of the variable transmission ratio module.

[0119] The input to the variable transmission ratio module is the longitudinal vehicle speed. Steering wheel angle and yaw rate gain In this embodiment, the yaw rate gain is set. The value is 0.31, and the output is the transmission ratio. .

[0120] When the longitudinal speed increases, the transmission ratio is increased to improve steering stability at high speeds and avoid oversteering.

[0121] Design transmission ratio :

[0122] when At that time, transmission ratio .

[0123] when At that time, transmission ratio .

[0124] when At that time, transmission ratio .

[0125] in, For vehicle stability factor, wheelbase Overall vehicle quality Distance from center of mass to front axle Distance from center of mass to rear axle Front axle lateral stiffness Rear axle lateral stiffness .

[0126] Ideal front wheel steering angle after changing gear ratio :

[0127]

[0128] Step 2.2: Feedforward compensation design based on neutral steering characteristics.

[0129] With neutral steering as the ideal driving state, feedforward compensation is used to reduce the deviation between the actual and ideal values ​​of the front wheel steering angle, and to correct the steering input in advance.

[0130] The input for feedforward compensation based on neutral steering characteristics is the longitudinal vehicle speed. and the ideal front wheel steering angle after changing the gear ratio in step 2.1 The output is the compensated ideal front wheel steering angle after feedforward compensation based on neutral steering characteristics. :

[0131]

[0132] The front wheel steering angle calculated from the ideal transmission ratio is pre-corrected to ensure that the ideal front wheel steering angle matches the vehicle speed, laying the foundation for subsequent control.

[0133] Step 3: Design a dual sliding mode controller for yaw rate and centroid sideslip angle based on the octopus optimization algorithm.

[0134] The Octopus optimization algorithm is used to collaboratively optimize the core parameters of the two controllers, enabling accurate calculation of the active front wheel compensation steering angle, and providing a control basis for subsequent weight allocation and steering execution.

[0135] Step 3.1: Design of yaw rate sliding mode controller.

[0136] Yaw rate is the angular velocity of a vehicle rotating about its longitudinal axis perpendicular to the ground. It is a core parameter describing the vehicle's steering dynamics, directly reflecting the speed and stability of the vehicle's rotation during steering. It is a key indicator that needs to be monitored and optimized in steering control.

[0137] Sliding mode control is a special type of nonlinear control method. Its core is to design and switch hyperplanes to make the system state converge from outside the hyperplane to the hyperplane and slide along the hyperplane to the control target. It has the advantages of fast response, strong anti-interference ability, no need for complex system identification and simple physical implementation.

[0138] The core function of the yaw rate sliding mode controller is to track the ideal yaw rate, reduce yaw rate tracking error, ensure vehicle trajectory tracking accuracy, and output the required ideal front wheel steering angle compensation angle through calculation.

[0139] The ideal yaw rate and ideal center of gravity sideslip angle are obtained based on the average adhesion coefficient obtained from the real-time road adhesion coefficients of the four tires output in step 1. Calculations using an ideal two-degree-of-freedom vehicle model yielded the following:

[0140]

[0141] in, For symbolic functions, For the ideal yaw rate, For the ideal centroid sideslip angle, It is the acceleration due to gravity. Take 9.8 m / s.

[0142] Modify the two-DOF model of the whole vehicle to include compensated steering angles. The differential equation for the yaw rate:

[0143]

[0144] in, yaw rate The first derivative, , , , This represents the moment of inertia of the entire vehicle about its vertical axis.

[0145] The approach law for the yaw rate sliding mode controller adopts a modified exponential approach law:

[0146]

[0147] Among them, sliding surface , For sliding surface The first derivative, the error between the actual yaw rate and the ideal yaw rate. , , where is the sliding surface parameter. For the reaching law coefficient, This is the chatter suppression coefficient. In this embodiment, it is taken as... , , , It is obtained by optimizing the octopus optimization algorithm in step 3.3.

[0148] Substituting the derivative expression of the sliding surface, we obtain the front wheel compensation angle based on the yaw rate:

[0149]

[0150] in, The centroid sideslip angle The first derivative, For ideal yaw rate The second derivative, Error between actual yaw rate and ideal yaw rate The first derivative, The ideal front wheel steering angle is compensated based on feedforward compensation of neutral steering characteristics. The first derivative.

[0151] Step 3.2, Design of the sliding mode controller for the centroid side slip angle.

[0152] The sideslip angle is the angle between the vehicle's center of gravity velocity direction and the vehicle's longitudinal axis, used to characterize the degree of deviation from the vehicle's driving posture. An excessively large sideslip angle can easily lead to vehicle sideslip and loss of control, and is one of the key parameters for assessing vehicle steering stability.

[0153] The core function of the center of gravity sideslip angle sliding mode controller is to suppress the center of gravity sideslip angle, control it within a reasonable range, ensure the lateral stability of the vehicle, and avoid vehicle sideslip and instability. It calculates and outputs the required ideal front wheel steering angle compensation angle.

[0154] Modify the two-DOF model of the whole vehicle to include compensated steering angles. The differential equation for the centroid side deflection angle is:

[0155]

[0156] in, yaw rate The first derivative, , , .

[0157] The approach law for the centroid sideslip angle sliding mode controller adopts a modified exponential approach law:

[0158]

[0159] Among them, sliding surface Error between actual centroid sideslip angle and ideal centroid sideslip angle , , where is the sliding surface parameter. For the reaching law coefficient, This is the chatter suppression coefficient. In this embodiment, it is taken as... , , , It is obtained by optimizing the octopus optimization algorithm in step 3.3.

[0160] Substituting the derivative expression of the sliding surface, we obtain the front wheel compensation angle based on the sideslip angle of the center of gravity:

[0161]

[0162] in, The centroid sideslip angle The first derivative, The centroid sideslip angle The second derivative, Error between actual yaw rate and ideal yaw rate The first derivative, The ideal front wheel steering angle is compensated based on feedforward compensation of neutral steering characteristics. The first derivative.

[0163] Step 3.3: Optimize the yaw rate and center of mass sideslip angle parameters of the dual sliding mode controller using the Octopus optimization algorithm.

[0164] The Octopus Optimization Algorithm is a biomimetic intelligent optimization algorithm based on the biological characteristics of octopuses, inspired by their distributed neural architecture, flexible tentacle movement, and environmental adaptability. This algorithm boasts advantages such as strong global search capabilities, good anti-interference performance, simple structure, and ease of engineering implementation, and is commonly used in scenarios such as optimizing various controller parameters.

[0165] The Octopus optimization algorithm was used to independently optimize the parameters of the yaw rate sliding mode controller and the center of mass sideslip angle sliding mode controller to address the differences in their control objectives.

[0166] By independently optimizing controller parameters, the control accuracy and robustness of a single controller are improved, laying the parameter foundation for subsequent weighted collaborative control of dual controllers.

[0167] With minimizing the root mean square error (RMSE) of the yaw rate tracking error as the sole objective, the four parameters of the yaw rate sliding mode controller in step 3.1 were optimized. The population size was set to 54, and the maximum number of iterations was set to 50. The optimal parameters were then obtained. , , , .

[0168] With minimizing the root mean square error (RMSE) of the centroid sideslip angle suppression error as the sole objective, the four parameters of the centroid sideslip angle sliding mode controller in step 3.2 were optimized. The population size was set to 54, and the maximum number of iterations was set to 50. The optimal parameters were then obtained. , , , .

[0169] The optimization results of the two controllers are independent of each other and have no parameter coupling. They can adjust the control intensity according to the vehicle's driving state, providing a flexible and accurate control basis for subsequent adaptive weight allocation based on phase plane analysis.

[0170] Step 4: Based on phase plane analysis, the yaw rate and the center of gravity sideslip angle are used to calculate the front wheel compensation angle output by the sliding mode controller. and Weights are adaptively allocated.

[0171] In this embodiment, a phase plane is constructed using the centroid sideslip angle and the rate of change of the centroid sideslip angle as the horizontal and vertical axes, respectively. By analyzing the motion trajectory of the system's state points on the phase plane, the dynamic characteristics, stability, and convergence of the system are intuitively reflected. The specific process is as follows: Figure 3 As shown.

[0172] Step 4.1: Construction of the phase plane double-line method.

[0173] Phase plane analysis is a graphical method for studying the stability of dynamic systems, and it is mainly applied to the analysis of first-order and second-order nonlinear differential equations.

[0174] Side slip angle at the center of mass With the rate of change of the centroid side deflection angle In the phase plane, the double-line method uses the saddle point, i.e. the unstable equilibrium point, of the vehicle's lateral dynamics system as the core reference for drawing the critical boundary.

[0175] First, based on the two-degree-of-freedom vehicle model and the tire nonlinear adhesion limit, the coordinates of the saddle point in the phase plane representing the critical point of vehicle instability are obtained. , Then, using the saddle point as the key node, two straight boundary lines with different slopes are fitted. Both lines pass through or are constrained at the saddle point, dividing the phase plane into an inner stable domain and an outer unstable domain, thereby realizing the construction of the vehicle's lateral dynamic stability boundary based on the saddle point.

[0176] The mathematical model for the stable boundary obtained by solving is as follows:

[0177]

[0178] in, and Here, are boundary coefficients, representing the slope and intercept of the linear stability boundary, respectively. Their values ​​are determined by the vehicle speed. Front wheel steering angle and average road surface adhesion coefficient Decide.

[0179] When the front wheel steering angle is not zero, the phase trajectory curve of the vehicle system does not converge to the origin, but converges to the equilibrium point where the centroid sideslip angle is a certain fixed value. At this time, the intercepts of the upper and lower boundaries of the stability domain are no longer symmetrical about the origin.

[0180] Therefore, considering the influencing factor of the front wheel steering angle, the mathematical model of the stability region is designed as follows:

[0181]

[0182] in, The intercept of the lower boundary of the stability region. It is the intercept of the upper boundary of the stable region. , , , The steady-state centroid sideslip angle.

[0183] To achieve rapid solution of stability domain parameters under all working conditions, discretization sampling was performed across all conditions. Seven discrete values ​​were taken for the front wheel steering angle within the range of 0–0.30 rad, with intervals of 0.05 rad; eight discrete values ​​were taken for the road adhesion coefficient within the range of 0.2–0.9, with intervals of 0.1 rad; and twelve discrete values ​​were taken for the vehicle speed within the range of 10–120 km / h, with intervals of 10 km / h, resulting in a total of 672 discrete working conditions. Different slopes were calculated for each discrete working condition. With intercept , .

[0184] For each fixed combination of vehicle speed and road surface adhesion coefficient, the slope at each of the seven front wheel steering angles was calculated. With intercept , Linear fitting was performed to obtain 96 sets of baseline slopes and intercepts.

[0185] Based on the above 96 sets of benchmark slopes and intercepts, a two-dimensional linear interpolation method is used to construct the slope. With intercept , The mapping relationship between vehicle speed and road surface adhesion coefficient continuously changes, realizing the slope across the entire working condition range. and intercept , Precise calculations.

[0186] Taking a typical working condition as an example, when the current wheel rotation angle is 0.01 rad, the vehicle speed is 60 km / h, and the road surface adhesion coefficient is 0.85, the phase plane is as follows: Figure 4 As shown, the calculated parameters are as follows: , , .

[0187] Step 4.2: Calculate the normalized distance.

[0188] Using the shortest distance formula from a point to a line, the normalized distance from the vehicle's current state point to the stability boundary is defined. The normalization range is set to [0,1]:

[0189]

[0190] in, ,Depend on and The maximum absolute value determines the value.

[0191] Normalized distance The closer the value is to 1, the closer the vehicle is to its instability limit. The closer the value is to 0, the more stable the vehicle is, and the higher its safety level. If the value is greater than 1, the vehicle will become unstable.

[0192] Step 4.3, Adaptive weight allocation.

[0193] Based on the normalized distance obtained in step 4.2 The control weights are adaptively adjusted.

[0194] when At this time, the vehicle is in a stable range and has high safety. The front wheel compensation angle is calculated by the optimized dual sliding mode controller.

[0195] when At this point, the vehicle's condition approaches or exceeds the stability boundary, and the vehicle is nearing the instability limit.

[0196] To achieve a smooth transition of the aforementioned control weights, the normalized distance is... Stability margin index mapped to the [0,1] interval :

[0197]

[0198] in, This is the function for finding the maximum value.

[0199] when At that time (i.e.) (), indicating that the current vehicle status is the most stable.

[0200] when At that time (i.e.) This indicates that the vehicle is currently close to or in a state of instability.

[0201] Weights for yaw rate tracking are dynamically generated using the Sigmoid nonlinear function. :

[0202]

[0203] in, The weights for yaw rate tracking, The weights for suppressing centroid sideslip angle. , To control the switching rate, take , Take the threshold center as the center. .

[0204] when hour, Increase the yaw rate, and the controller will prioritize tracking the yaw rate.

[0205] when hour, The angle decreases rapidly, and the controller prioritizes suppressing the centroid sideslip angle.

[0206] Step 4.4: Calculate the total compensation angle.

[0207] Based on the adaptive weights obtained in step 4.3 and The compensation angle of the dual sliding mode controller output and The total compensated turning angle is obtained by performing smooth weighted fusion. :

[0208]

[0209] When the vehicle is in a high stability margin range ( ,Right now ), Approaching 1 Approaching 0, the total compensation angle is Prioritize ensuring the accuracy of yaw rate tracking.

[0210] When vehicle stability margin decreases ( ,Right now )hour, Rapid decay Approaching 1, the total compensation angle is Prioritize suppressing the center of gravity sideslip angle to ensure the vehicle's lateral stability.

[0211] When the vehicle is at the critical point of instability or in an unstable state ( ,Right now )hour, The total compensation angle is entirely output by the center of gravity sideslip angle sliding mode controller, which forcibly pulls the vehicle back to the stable range, achieving a balance between maneuverability and stability under all working conditions.

[0212] Step 4.5: The Prime Minister turns the front wheel to the side.

[0213] The total compensation angle obtained in step 4.4 Ideal front wheel steering angle after feedforward compensation based on neutral steering characteristics, input by the driver. By superimposing these values, the total ideal front wheel steering angle output by the active front wheel steering system to the steering actuator is obtained. :

[0214]

[0215] Step 5: Design of the steering actuator.

[0216] The steer-by-wire actuator is the physical actuator for active front wheel steering control. Its core function is to receive the total ideal front wheel steering angle output by the controller. The front wheel steering angle is rapidly tracked through closed-loop control of the DC motor.

[0217] Step 5.1: Calculate the corner tracking error.

[0218] Based on the pre-processed overall ideal front wheel steering angle To control the target, the actual front wheel steering angle The corner tracking error is calculated using the difference as the closed-loop feedback value. :

[0219]

[0220] Step 5.2, PID control module.

[0221] Corner tracking error The input is fed into the PID closed-loop control module, which calculates the proportional, integral, and derivative control terms of the error, respectively. The sum of these three terms outputs the target control voltage for the DC motor that drives the direction of rotation. .

[0222] Among them, the control parameters of the PID control module after simulation calibration include: proportional adjustment term. Integral adjustment item Differential adjustment term .

[0223] Step 5.3: DC motor dynamic response and gear and rack displacement conversion.

[0224] The target control voltage output by the PID module The input is fed into the DC motor model and converted into the linear displacement of the rack in the rack and pinion steering mechanism. This completes the conversion from electrical signal control input to mechanical displacement output of the steering actuator.

[0225] Step 5.4: Nonlinear mapping between rack displacement and front wheel rotation angle.

[0226] The real-time displacement of the rack obtained in step 5.3 As input, the nonlinear mapping solution is completed by looking up a table.

[0227] If the input rack displacement If the value does not fall on a discrete calibration node in the mapping table, linear interpolation is used to perform continuous calculation, ultimately obtaining the actual front wheel steering angle value of the vehicle that precisely corresponds to the current rack displacement. .

[0228] This invention employs a dual sliding mode cooperative control method based on road surface recognition and adaptive weight allocation of the phase plane stability domain. This method can accurately adapt to different road surface adhesion conditions and vehicle driving conditions, effectively suppress the chattering problem of sliding mode control, and balance the trajectory tracking accuracy and lateral stability of vehicle steering. It solves the technical problems of poor road surface adaptability, difficulty in dynamically balancing maneuverability and stability, and coupling interference of multiple control targets in traditional active front wheel steering control. It is particularly suitable for real-time active front wheel steering control of automotive steer-by-wire systems.

[0229] The method of this invention was used to conduct simulation verification under the ISO double lane change standard working condition. The total driving time of the working condition was 15s, and the vehicle speed was 60km / h (16.7m / s). Figure 5 The comparison curves show the vehicle yaw rate with and without AFS control strategy, including yaw rate without control, ideal yaw rate, and yaw rate under the AFS control method of this invention. After adopting the method of this invention, the yaw rate curve highly coincides with the ideal value, the peak overshoot is significantly reduced, the dynamic response process is smooth without obvious fluctuations, and the trajectory tracking accuracy and handling response characteristics of the vehicle steering process are effectively improved. Figure 6 The curves show a comparison of the vehicle's center of gravity sideslip angle with and without the AFS control strategy, including the sideslip angle under no control and the sideslip angle under the AFS control method of this invention. After adopting the method of this invention, the peak value of the sideslip angle is significantly reduced and the fluctuation range is greatly narrowed, effectively avoiding the risk of vehicle sideslip and instability, and greatly improving the lateral driving stability of the vehicle under extreme conditions. Figure 7The curves show a comparison of the front wheel steering angles of vehicles with and without AFS control strategy, including the front wheel steering angles in the uncontrolled state and under the AFS control method of this invention. Using the method of this invention, the peak value of the output front wheel steering angle is smaller, ensuring both the rapid response capability of the steering system and avoiding excessively large steering angle outputs.

[0230] In summary, compared with the prior art, the present invention has the following characteristics:

[0231] 1. This invention is based on real-time and accurate identification of road surface adhesion coefficient. It can dynamically adapt the control parameters of the steer-by-wire system according to the identification results, which solves the problem that the traditional steer-by-wire AFS control adopts a fixed parameter strategy and has poor matching degree with complex road conditions. It greatly improves the adaptability of the control strategy to different road conditions.

[0232] 2. The sliding mode controller of this invention employs an improved exponential reaching law, replacing the traditional sign function with a combination of a hyperbolic tangent function and a chatter suppression coefficient. This significantly reduces the inherent high-frequency chattering of sliding mode control while maintaining the reaching speed. It resolves the contradiction between the slow convergence of the traditional exponential reaching law near the sliding surface and the difficulty in balancing chatter suppression and response speed, thus improving the stability and accuracy of front wheel steering angle control.

[0233] 3. This invention employs the Octopus Optimization Algorithm, with the yaw rate tracking error and the center of mass sideslip angle suppression effect as optimization objectives. It optimizes the core parameters of the dual sliding mode controller for yaw rate and center of mass sideslip angle, solving the limitations of traditional single sliding mode controllers and the industry pain points of parameter dependence on manual offline calibration and poor adaptability to multiple working conditions. The optimized controller can effectively suppress high-frequency chattering in sliding mode, while also taking into account control response speed and anti-interference robustness.

[0234] 4. Based on the phase plane stability analysis results of the vehicle's center of gravity sideslip angle-center of gravity sideslip angle change rate, this invention achieves adaptive dynamic weight allocation for the two front wheel compensation steering angles output by the dual sliding mode controller. This solves the inherent control contradiction of the existing technology, which uses a fixed weight allocation method and cannot balance the yaw rate tracking accuracy and center of gravity sideslip angle stability. The priority of the two control targets can be dynamically adjusted according to the real-time stability state of the vehicle, realizing the coordinated optimization of trajectory tracking and driving stability.

[0235] The above description discloses only one preferred embodiment of the present invention, and should not be construed as limiting the scope of the present invention. Those skilled in the art will understand that all or part of the processes of the above embodiments can be implemented, and equivalent changes made in accordance with the claims of the present invention are still within the scope of the invention.

Claims

1. A method for controlling active front wheel steering of an automobile by steer-by-wire, characterized in that, Includes the following steps: Step 1: Use the unscented Kalman filter algorithm combined with the Dugoff tire model to identify the road surface adhesion coefficient, providing basic road condition parameters for subsequent control strategies; Step 2: Design the feedforward compensation angle for the front wheel steering angle based on the variable transmission ratio and neutral steering characteristics; Step 3: Taking the yaw rate tracking error and the center of gravity sideslip angle suppression effect as optimization objectives, the Octopus optimization algorithm is used to optimize the core control parameters of the yaw rate sliding mode controller and the center of gravity sideslip angle sliding mode controller, so as to provide a stable closed-loop control input for subsequent weight allocation and steering execution. Step 4: Based on the phase plane analysis results of the vehicle's center of gravity sideslip angle and the rate of change of the center of gravity sideslip angle, the weights of the yaw rate and the front wheel compensation angle output by the center of gravity sideslip angle sliding mode controller are adaptively allocated. The allocated total compensation angle is then superimposed with the ideal front wheel angle after feedforward compensation based on the driver's input of neutral steering characteristics, and the final output is the total ideal front wheel angle of the steer-by-wire system. Step 5: Input the desired front wheel angle as the target value into the steer-by-wire system, and achieve rapid tracking of the front wheel angle through closed-loop control of the DC motor, thus completing the full-process closed-loop control of active front wheel steering.

2. The steer-by-wire active front wheel steering control method for automobiles as described in claim 1, characterized in that, In step 1, the average of the road adhesion coefficients identified by the four tires is taken as the current road adhesion coefficient value.

3. The steer-by-wire active front wheel steering control method for automobiles as described in claim 1, characterized in that, Step 3, obtaining the front wheel compensation angle based on the yaw rate, includes the following steps: The ideal yaw rate and ideal center-of-gravity sideslip angle are obtained from the average adhesion coefficient of the four tires' real-time road adhesion coefficients output in step 1. Calculated using an ideal two-degree-of-freedom vehicle model; The two-degree-of-freedom model of the vehicle was modified to include a yaw rate differential equation with compensation for steering angle: The approach law for the yaw rate sliding mode controller adopts a modified exponential approach law: Substituting the derivative expression of the sliding surface, we obtain the front wheel compensation angle based on the yaw rate: in, yaw rate The first derivative, , , Sliding surface , For sliding surface The first derivative, the error between the actual yaw rate and the ideal yaw rate. , For the ideal yaw rate, , where is the yaw rate sliding surface parameter. The coefficient of the yaw rate approach law. This is the yaw rate chattering suppression coefficient. The centroid sideslip angle The first derivative, For ideal yaw rate The second derivative, Error between actual yaw rate and ideal yaw rate The first derivative, The ideal front wheel steering angle is compensated based on feedforward compensation of neutral steering characteristics. The first derivative, For longitudinal vehicle speed, Wheelbase The distance from the center of gravity to the front axle. The distance from the center of gravity to the rear axle. For front axle lateral stiffness, For rear axle lateral stiffness, This represents the moment of inertia of the entire vehicle about its vertical axis.

4. The steer-by-wire active front wheel steering control method for automobiles as described in claim 1, characterized in that, Step 3, obtaining the front wheel compensation angle based on the center of gravity sideslip angle, includes the following steps: The two-degree-of-freedom model of the vehicle was modified to include the differential equation for the center of mass sideslip angle with compensation for rotation: The approach law for the centroid sideslip angle sliding mode controller adopts a modified exponential approach law: Substituting the derivative expression of the sliding surface, we obtain the front wheel compensation angle based on the sideslip angle of the center of gravity: in, , , Sliding surface Error between actual centroid sideslip angle and ideal centroid sideslip angle , For the ideal centroid sideslip angle, , where is the sliding surface parameter for the centroid side slip angle. The coefficient of the centroid sideslip angle approach law. This is the flutter suppression coefficient for the center of mass sideslip angle. The centroid sideslip angle The first derivative, For the ideal centroid sideslip angle The second derivative, The error between the actual centroid sideslip angle and the ideal centroid sideslip angle The first derivative, The ideal front wheel steering angle is compensated based on feedforward compensation of neutral steering characteristics. The first derivative, For the overall vehicle weight.

5. The steer-by-wire active front wheel steering control method for automobiles as described in claim 1, characterized in that, The octopus optimization algorithm parameter optimization step specifically involves optimizing the yaw rate and center-of-gravity sideslip angle parameters of the dual sliding mode controller using the octopus optimization algorithm. The goal is to minimize the root mean square error of the yaw rate tracking error and the root mean square error of the center-of-gravity sideslip angle suppression error, respectively, by optimizing the sliding surface parameters. , reaching law coefficient , Boom suppression coefficient , To find the best option.

6. The steer-by-wire active front wheel steering control method for automobiles as described in claim 4, characterized in that, Step 4 uses normalized distance and stability margin to calculate and optimize the adaptive weight allocation, including the following steps: Step 4.1: At the centroid side slip angle With the rate of change of the centroid side deflection angle In the phase plane, the double-line method uses the saddle point, i.e. the unstable equilibrium point, of the vehicle's lateral dynamics system as the core reference to draw the critical boundary; Discrete sampling was performed on the front wheel steering angle, road surface adhesion coefficient, and vehicle speed to calculate the slope under each working condition. intercept with the lower boundary of the stability region intercept of the upper boundary of the stability region The baseline data is obtained by linear fitting of the parameters under a fixed vehicle speed and adhesion coefficient, and then the parameter mapping relationship is established by two-dimensional linear interpolation. Step 4.2: Using the shortest distance formula from a point to a line, define the normalized distance from the vehicle's current state point to the stability boundary. The normalization range is set to [0,1]; Step 4.3: Based on the normalized distance It can make adaptive adjustments.

7. The steer-by-wire active front wheel steering control method for automobiles as described in claim 6, characterized in that, Step 4.3 The adaptive adjustment strategy is as follows: Normalized distance Stability margin index mapped to the [0,1] interval ; Weights for yaw rate tracking are dynamically generated using the Sigmoid nonlinear function. : in, The weights for yaw rate tracking, The weights for suppressing centroid sideslip angle. , To control the switching rate, The threshold center; when hour, Increase the yaw rate, and the controller will prioritize tracking the yaw rate; when hour, The angle decreases rapidly, and the controller prioritizes suppressing the centroid sideslip angle.

8. The steer-by-wire active front wheel steering control method for automobiles as described in claim 7, characterized in that, Step 4 also includes the steps of weighted fusion of compensated corner angles and output of total corner angle: The total compensation angle calculation is based on the adaptive weights obtained in step 4.3, which affect the compensation angle output by the dual sliding mode controller. and The total compensated turning angle is obtained by performing smooth weighted fusion. ; When the vehicle is in a high stability margin range, the total compensation angle is... Prioritize ensuring yaw rate tracking accuracy; when vehicle stability margin decreases, the total compensation angle is... The main function is to prioritize suppressing the center of gravity sideslip angle to ensure the vehicle's lateral stability. When the vehicle is at the critical point of instability or in an unstable state, the total compensation angle is output entirely by the center of gravity sideslip angle sliding mode controller, which forcibly pulls the vehicle back to the stable range. The Prime Minister wants to include the total compensation angle from the total compensation angle calculation in the front wheel steering angle calculation. Ideal front wheel steering angle after feedforward compensation based on neutral steering characteristics, input by the driver. By superimposing these values, the total ideal front wheel steering angle output by the active front wheel steering system to the steering actuator is obtained. .

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