Steering control method and device

Abstract

The present application relates to the field of vehicle control, and provides a steering control method and device. The steering control method comprises constructing a whole vehicle model and combining initial perception parameters of the whole vehicle to solve vehicle state; calculating preset steering angle of an axle based on a preset target function; obtaining real-time perception parameters of the whole vehicle and real-time steering angle of the axle to form closed-loop control. The steering control method solves the problem of large measurement error of a single sensor, improves the accuracy of vehicle state estimation; can also accurately match steering requirements under different vehicle speeds and road conditions, and the trajectory tracking lateral deviation can be controlled within a preset range, which is significantly better than the traditional steering control method; the closed-loop control can quickly respond to road interference by real-time correction of steering deviation, and reduce steering lag and steering response time in emergency obstacle avoidance.

Description

Cross-references to related applications

[0001] This application claims priority to Chinese Patent Application No. 2025112231700, filed on August 28, 2025, entitled “Steering Control Method and Apparatus”, the entirety of which is incorporated herein by reference. Technical Field

[0002] This invention relates to the field of vehicle control, and provides a steering control method and apparatus. Background Technology

[0003] Current articulated multi-vehicle units rely primarily on the steering function of the front wheels at high speeds, while the other axles employ a steering lock mode. During high-speed travel on non-straight roads, the vehicle primarily relies on the lateral slip characteristics of the tires to follow the small steering inputs of the preceding vehicle unit, utilizing the lateral stiffness of the tires to ensure lateral stability.

[0004] However, this steering lock mode makes the vehicle rely entirely on the tire's lateral characteristics and lateral stiffness to achieve yaw and resist external disturbances. This not only causes the tire to generate more drag at high speeds, thus accelerating tire wear, but may also be detrimental to the vehicle's handling response and driving comfort. Summary of the Invention

[0005] This invention provides a steering control method to address the shortcomings of low vehicle steering accuracy in related technologies.

[0006] This invention also provides a steering control device.

[0007] A first aspect of the present invention provides a steering control method, comprising: Construct a vehicle model and calculate the vehicle state by combining the initial perception parameters of the vehicle; The preset steering angle of the axle is calculated based on a preset objective function; The system acquires real-time sensing parameters of the entire vehicle and real-time steering angles of the axles to form a closed-loop control.

[0008] According to one embodiment of the present invention, in the step of constructing the vehicle model, The vehicle model is simplified into a six-degree-of-freedom articulated vehicle model; The six-degree-of-freedom model of the articulated vehicle is constructed using the following formula: ; Among them, state variables x =[ β1γ1γ2γ3θ1θ2 ] T ; System input δ =[ δ1δ2δ3δ4δ5δ6] T .

[0009] According to an embodiment of the present invention, the state quantity x middle, β The sideslip angle is the center of gravity of the vehicle body. γ Let be the yaw rate of the vehicle body, and be the hinge angle between adjacent vehicle bodies.

[0010] According to one embodiment of the present invention, the input quantity of the system δ middle, δ1 to δ6 The hinge angle between vehicle units.

[0011] According to one embodiment of the present invention, in the step of constructing a vehicle model and calculating the vehicle state in combination with the initial perception parameters of the vehicle, The initial sensing parameters include the vehicle's center of gravity sideslip angle. β Hinge angle between adjacent vehicle bodies θ and the steering angle of the non-driver operating axis δ .

[0012] According to one embodiment of the present invention, the preset objective function is a cost function, and the expression of the cost function is: ; in, β1 , β2 , β3 The sideslip angles are the centers of gravity of the three vehicle bodies. θ1 , θ2 The hinge angle between adjacent vehicle bodies. δx This refers to the steering angle of the non-driver operated axis.

[0013] According to one embodiment of the present invention, the quadratic form matrix of the cost function is: ; in, It is a symmetric matrix. It is a unit array.

[0014] According to one embodiment of the present invention, the control feedback quantity is solved based on the cost function in quadratic matrix form. δx ,set up δx =- Kx ,in K 5×6 For the feedback matrix, x =[ β1γ1γ2γ3θ1θ2 ] T It is a state variable.

[0015] According to an embodiment of the present invention, the feedback matrix KBy solving the Riccati equation, we obtain the following: ; in, P 6×6 Let be a solution to the Ricardi equation, and let the feedback matrix be... K = R -1 F T P The steering angle of the non-driver operating shaft is: .

[0016] A second aspect of the present invention provides a steering control device, comprising: The solution module is used to construct the vehicle model and solve the vehicle state by combining the initial perception parameters of the vehicle. The calculation module is used to calculate the preset steering angle of the axle based on a preset objective function; The control module is used to acquire the real-time sensing parameters of the whole vehicle and the real-time steering angle of the axle to form a closed-loop control.

[0017] The steering control method provided by the first aspect of the present invention solves the problem of large measurement errors of a single sensor by combining a whole vehicle model with the state calculation of a Kalman filter algorithm, thereby improving the accuracy of vehicle state estimation. Based on multi-objective optimization, the preset steering angle calculation can accurately match steering requirements under different vehicle speeds and road conditions, and the lateral deviation of trajectory tracking can be controlled within a preset range, significantly outperforming traditional steering control methods. The side slip angle constraint and yaw rate tracking mechanism set in the preset objective function can effectively suppress the risk of sideslip and fishtailing of the vehicle under rapid acceleration, sharp turns, or low-adhesion road surfaces. Closed-loop control, by correcting steering deviation in real time, can quickly respond to road disturbances, reducing steering lag and steering response time during emergency obstacle avoidance. The smoothness constraint on the rate of change of steering angle in the objective function reduces the impact during steering; simultaneously, through accurate modeling of suspension and tire characteristics using a whole vehicle model, the differences in steering characteristics under different loads can be compensated, ensuring the consistency of steering feel throughout the vehicle's entire lifespan. This method is compatible with different types of vehicles through modular design, and can be adapted by simply adjusting the parameters of the whole vehicle model; the multi-source fusion mechanism of real-time perception parameters supports sensor redundancy and fault diagnosis, improving system reliability; in addition, the preset objective function can dynamically adjust the weights according to user needs, and has flexible personalized configuration capabilities.

[0018] According to the steering control device provided in the second aspect of the present invention, the device adopts an independent design of three modules: solution, calculation, and control. Each module communicates through a standardized interface, reducing system coupling. When a module fails, basic system functions can be ensured through module-level redundancy switching, improving overall reliability. Simultaneously, the modular structure facilitates future upgrades, reducing maintenance costs. Real-time feedback correction ensures that the deviation between the actual steering angle and the preset value is controlled within a preset angle range; furthermore, the real-time monitoring function can quickly identify abnormal states and trigger safety protection mechanisms, reducing the risk of vehicle loss of control. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this 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 some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 This is a schematic flowchart of the steering control method provided by the present invention.

[0021] Figure 2 This is a schematic diagram of the state parameters of the steering control method provided by the present invention.

[0022] Figure 3 This is a schematic structural diagram of the steering control device provided by the present invention.

[0023] Figure label: 100. Solving module; 200. Calculation module; 300. Control module. Detailed Implementation

[0024] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and should not be construed as limiting the scope of the invention.

[0025] like Figures 1 to 2 As shown, a first aspect of the present invention provides a steering control method, comprising: Step 10: Construct a vehicle model and calculate the vehicle state by combining the initial perception parameters of the vehicle. Step 20: Calculate the preset steering angle of the axle based on the preset objective function; Step 30: Obtain the real-time sensing parameters of the whole vehicle and the real-time steering angle of the axle to form a closed-loop control.

[0026] The steering control method provided by the first aspect of the present invention solves the problem of large measurement errors of a single sensor by combining a whole vehicle model with the state calculation of a Kalman filter algorithm, thereby improving the accuracy of vehicle state estimation. Based on multi-objective optimization, the preset steering angle calculation can accurately match steering requirements under different vehicle speeds and road conditions, and the lateral deviation of trajectory tracking can be controlled within a preset range, significantly outperforming traditional steering control methods. The side slip angle constraint and yaw rate tracking mechanism set in the preset objective function can effectively suppress the risk of sideslip and fishtailing of the vehicle under rapid acceleration, sharp turns, or low-adhesion road surfaces. Closed-loop control, by correcting steering deviation in real time, can quickly respond to road disturbances, reducing steering lag and steering response time during emergency obstacle avoidance. The smoothness constraint on the rate of change of steering angle in the objective function reduces the impact during steering; simultaneously, through accurate modeling of suspension and tire characteristics using a whole vehicle model, the differences in steering characteristics under different loads can be compensated, ensuring the consistency of steering feel throughout the vehicle's entire lifespan. This method is compatible with different types of vehicles through modular design, and can be adapted by simply adjusting the parameters of the whole vehicle model; the multi-source fusion mechanism of real-time perception parameters supports sensor redundancy and fault diagnosis, improving system reliability; in addition, the preset objective function can dynamically adjust the weights according to user needs, and has flexible personalized configuration capabilities.

[0027] Please continue reading Figure 1 and Figure 2 The steering control method provided in the first aspect of the present invention achieves precise steering control of a multi-unit articulated vehicle through a three-order process of "model building - target optimization - closed-loop control".

[0028] Specifically, in step 10, a vehicle model is constructed and the vehicle state is calculated by combining the initial perception parameters; Based on multibody dynamics theory, a whole vehicle model is constructed that incorporates the core physical characteristics of the vehicle. The model covers key parameters such as wheelbase, track width, center of gravity position, and body moment of inertia, ensuring that the model can realistically reflect the dynamic response of the vehicle under different operating conditions.

[0029] Initial perception data is collected through onboard sensors. This initial perception data may include, for example, the vehicle's initial speed, initial steering angle, initial yaw rate, initial longitudinal / lateral acceleration, estimated road surface adhesion coefficient, and information on surrounding environmental obstacles.

[0030] The initial sensing parameters are input into the vehicle model to obtain the real-time core state quantities of the vehicle, including precise longitudinal velocity, lateral velocity, center of gravity sideslip angle, yaw rate, and vertical load of each wheel, which provide the basic state basis for subsequent steering angle calculation.

[0031] In step 20, the preset steering angle of the axle is calculated based on the preset objective function; The objective function comprehensively considers vehicle driving safety, handling stability, and ride comfort. Using the vehicle state variables calculated in step 10 as input, the preset objective function is solved through rolling optimization based on the model. Multiple candidate steering angle sequences are generated, and the optimal solution is selected by evaluating the objective function values ​​corresponding to each sequence, thus determining the preset steering angle of the axle at the current moment.

[0032] In step 30, real-time sensing parameters and real-time steering angle are acquired to form closed-loop control; Real-time sensing data is continuously collected by high-frequency sensors, including real-time vehicle speed, real-time lateral acceleration, real-time yaw rate, real-time road surface adhesion coefficient, and current position feedback of steering actuators. Compared with the initial sensing parameters, the real-time parameters have higher timeliness and dynamic response capability.

[0033] The actual steering angle of the axle is collected in real time by the angle sensor or actuator position feedback device built into the steering system, which accurately reflects the current output state of the steering actuator.

[0034] The deviation between the preset steering angle and the real-time steering angle is calculated. Combined with the disturbance terms in the real-time sensing parameters, the steering correction amount is generated by PID control or sliding mode control algorithm and output to the steering actuator. The steering motor torque or steering tie rod displacement is dynamically adjusted so that the actual steering angle can quickly track the preset steering angle. At the same time, the vehicle model parameters are updated according to the real-time state deviation through the online model correction mechanism to ensure the stability of control accuracy under complex working conditions.

[0035] According to one embodiment of the present invention, in the step of constructing a complete vehicle model, The vehicle model is simplified into a six-degree-of-freedom articulated vehicle model; The six-degree-of-freedom model of the articulated vehicle is constructed using the following formula: ; Among them, state variables x =[ β1γ1γ2γ3θ1θ2 ] T ; System input δ =[ δ1δ2δ3δ4δ5δ6 ] T .

[0036] In one embodiment of the present invention, the vehicle model is simplified to a six-degree-of-freedom dynamic model for multi-unit articulated vehicles (such as 3-car bus or light rail trains), with the degrees of freedom including the three body center sideslip angles. β1、β2、β3 ), the yaw rate of the three car bodies ( γ1, γ2, γ3 ) and the hinge angle between two adjacent vehicle bodies ( θ1, θ2 A total of 6 state variables constitute the state vector. x System input quantities δThe control amount of the articulation angle between the 6 vehicle units ( δ1 to δ6 ), which corresponds to the control input of each unit's steering actuator.

[0037] In the model equations, matrix A is a 6×6 system matrix, matrix B is a 6×6 state feedback matrix, and matrix C is a 6×6 input gain matrix, which are derived through vehicle dynamics theory and calibrated using actual vehicle parameters.

[0038] The six-degree-of-freedom model encompasses key dynamic parameters such as center of gravity sideslip, yaw, and hinge angle. Compared to simplified models, it more comprehensively reflects the coupled motion characteristics of multi-unit vehicles, providing a reliable theoretical foundation for state calculation. While retaining core dynamic characteristics, it simplifies model complexity, avoiding the high computational load of full-degree-of-freedom models and ensuring rapid calculation capabilities in real-time control scenarios. It specifically considers the influence of hinge angles between adjacent vehicle bodies, overcoming the limitations of traditional single-body models and making it more suitable for the steering control needs of multi-unit vehicles such as buses and trains.

[0039] According to an embodiment of the present invention, state variables x middle, β The sideslip angle is the center of gravity of the vehicle body. γ Let be the yaw rate of the vehicle body, and be the hinge angle between adjacent vehicle bodies.

[0040] In one embodiment of the present invention, state quantity x The physical definitions of the parameters are as follows: centroid sideslip angle β The angle between the vehicle's center of gravity velocity direction and the vehicle's longitudinal axis is measured by a GPS integrated navigation system installed at the vehicle's center of gravity; yaw rate. γ The angular velocity of the vehicle body about its vertical axis is collected by an inertial measurement unit on the top of the vehicle body; hinge angle. θ The angle between the longitudinal axes of two adjacent vehicle body sections is measured using an absolute encoder at the hinge. All parameters are fused using a Kalman filter to eliminate measurement noise and ensure the accuracy of the state parameters.

[0041] β, γ, θ These three parameters—lateral offset, rotational motion, and relative attitude between units—work together to form a complete description of the vehicle's dynamic state, providing multi-dimensional feedback for steering control. Multi-sensor fusion and filtering ensure minimal parameter measurement errors, maintaining stable output even under complex road conditions and avoiding control misjudgments caused by noise. The introduction of the articulation angle θ allows the model to capture the motion coupling relationship between units, providing key parameters for multi-unit cooperative steering and reducing tension and impact between units.

[0042] According to one embodiment of the present invention, the system input quantity δ middle, δ1 to δ6 The hinge angle between vehicle units.

[0043] In one embodiment of the present invention, the input quantity δ of δ1 to δ6 The six steering control nodes corresponding to a 3-car train: δ1, δ2 This refers to the control amount of the left and right hinge angle between the first and second body sections. δ3, δ4 This refers to the control amount of the left and right hinge angle between the second and third body sections. δ5, δ6 This refers to the hinge angle control parameters for the steering shaft at the end of the third section of the vehicle body. Each hinge angle control parameter represents the target angle of the steering actuator, achieved through an electro-hydraulic servo valve or an electric push rod. The input parameters and the transmission ratio of the vehicle body steering mechanism are pre-calibrated and stored in the controller parameter table.

[0044] Multiple inputs correspond to the steering nodes of different units, allowing independent adjustment of the steering attitude of each unit. This overcomes the limitations of traditional single-input control and improves handling flexibility in complex road conditions. The inputs are directly related to the actuator control angle, and transmission ratio calibration eliminates the influence of nonlinear mechanisms, ensuring consistency between control commands and actual steering angles and reducing control deviations. The multi-input layout allows for compensation and adjustment through other inputs in case of a single actuator failure, enhancing system fault tolerance and driving safety.

[0045] According to one embodiment of the present invention, in the step of constructing a vehicle model and calculating the vehicle state by combining the initial perception parameters of the vehicle, Initial perception parameters include the vehicle's center of gravity sideslip angle. β Hinge angle between adjacent vehicle bodies θ and the steering angle of the non-driver operating axis δ .

[0046] In one embodiment of the present invention, the initial sensing parameters are acquired collaboratively by multiple sensors: centroid sideslip angle. β Calculated by fusion of IMU and wheel speed sensor; hinge angle θ The steering angle of the non-driver operated shaft is directly measured by a magnetostrictive displacement sensor at the hinge. δ Data is collected by an angle sensor installed on the steering knuckle.

[0047] The initial parameters collected can be preprocessed before solution: outliers are removed, smoothed and filtered, and aligned with the initial state of the vehicle model as the initial conditions for state solution.

[0048] Multi-dimensional initial parameters cover key vehicle states, providing comprehensive initial conditions for model solution and reducing convergence time and error in state estimation. Sensor fusion and preprocessing eliminate noise and outlier interference, ensuring that initial parameters remain stable and effective in complex environments and preventing solution divergence. It supports solution requirements for different initial states such as stationary and low-speed driving, without forcing the vehicle to be in a specific initial posture, enhancing the method's practicality.

[0049] According to one embodiment of the present invention, the preset objective function is a cost function, and the expression of the cost function is: ; in, β1 , β2 , β3 The sideslip angles are the centers of gravity of the three vehicle bodies. θ1 , θ2 The hinge angle between adjacent vehicle bodies. δx This refers to the steering angle of the non-driver operated axis.

[0050] In one embodiment of the present invention, the cost function J The optimization objective used to quantify vehicle steering control is to minimize J Achieve dual-objective control of "vehicle stability + smooth steering". In the function, the squared term of the centroid sideslip angle ( β1 2 、β2 2 、β3 2 The weights are the same, used to suppress lateral body offset; the square term of the hinge angle ( θ1 2 , θ2 2 The weight can be 1.2 times that of the sideslip angle term, mainly limiting excessive relative rotation between adjacent vehicle bodies; the square of the non-driver operating axis steering angle term ( δx 2 The weight can be 0.8 times that of the sideslip angle term to avoid excessive tire wear and energy consumption caused by excessive steering angle. The weight is calibrated through real vehicle testing and supports dynamic adjustment.

[0051] By integrating stability, structural protection, and economic objectives through a weighted sum of squares, the design avoids the problem of sacrificing one aspect for another due to single-objective optimization. The squared term design imposes a stronger penalty on large deviation parameters, effectively preventing vehicle instability or structural damage caused by excessive sideslip angles or articulation angles, thus improving driving safety. The adjustable weights allow the cost function to adapt to different road conditions and load scenarios, enhancing the robustness of the control method.

[0052] According to one embodiment of the present invention, the quadratic form matrix of the cost function is: ; in, It is a symmetric matrix. It is a unit array.

[0053] In one embodiment of the present invention, the state weight matrix is ​​included in the quadratic matrix cost function. Q It is a 6×6 symmetric positive definite matrix, and the diagonal elements correspond to state variables. x Weights: Q(1,1) = Q(2,2) = Q(3,3) = 1.0 (weights of β1, β2, β3) weights , Q(4,4) = Q(5,5) = 1.2 (weights of γ1, γ2) , Q(6,6) = 1.2 (weight of θ1) Off-diagonal elements are 0. Control weight matrix. R for 5×5 Unit Array (R = I5×5) The diagonal elements are all 0.8, used to limit the steering angle of the non-driver operating axis. δx The magnitude. Matrix Q and R The system poles are determined by trial and error combined with the theory of linear quadratic regulators (LQR) to ensure that the system poles are located in the left half of the s-plane.

[0054] The quadratic matrix form allows the cost function to be solved for the optimal control quantity using the LQR method, avoiding the computational burden of complex nonlinear optimization and ensuring the feasibility of real-time control. Q and R The element settings intuitively reflect the importance of each state variable and control variable, facilitating the optimization of control performance by adjusting matrix elements. The positive definite matrix design ensures that the cost function has a unique minimum, and combined with the pole placement method, it gives the closed-loop system excellent dynamic response characteristics.

[0055] According to one embodiment of the present invention, the control feedback quantity is solved based on a cost function in quadratic matrix form. δx ,set up δx =- Kx ,in K 5×6 For the feedback matrix, x =[ β1γ1γ2γ3θ1θ2 ] T It is a state variable.

[0056] In one embodiment of the present invention, the control feedback quantity is controlled. δx For steering angle commands of non-driver operated axes, the form of status feedback is used. δx = -Kx To achieve closed-loop control, the feedback matrix... K for 5×6 A constant matrix (5 rows corresponding to 5 control variables, 6 columns corresponding to 6 state variables). K The elements are solved using the LQR algorithm, based on the state weight matrix. Q and control weight matrix R Calculated.

[0057] Specifically, in MATLAB, the LQR function is called, and the system matrix is ​​input. A Input matrix B Weight matrix Q and R Output the optimal feedback matrix K This ensures the stability of the closed-loop system's transfer function. State variables. x The data is collected in real time by sensors and then filtered before being input into the feedback formula. The calculation cycle is consistent with the model sampling time.

[0058] The state feedback mechanism is simple and intuitive, with low computational complexity, allowing for efficient execution in embedded controllers and meeting the real-time requirements of vehicle dynamic control. Full-state feedback effectively suppresses external disturbances such as road surface disturbances and parameter perturbations, enhancing the robustness of steering control. Fixed feedback matrix. K This avoids complex online optimization processes, reduces controller hardware requirements, and facilitates engineering implementation and mass application.

[0059] According to one embodiment of the present invention, the feedback matrix K By solving the Riccati equation, we obtain the following equation: ; in, P 6×6 Let be a solution to the Ricardi equation, and let the feedback matrix be... K = R -1 F T P The steering angle of the non-driver operating shaft is: .

[0060] In one embodiment of the present invention, the Riccati equation is solved for discrete-time systems, and the matrix in the equation... A -1 B The dynamic matrix of a 6×6 system is... F for 5×6 Input matrix, matrix P for 6×6 Symmetric positive definite solution matrix.

[0061] The solution process was completed offline: based on the calibrated vehicle parameter initialization matrix, the Riccati equation was solved using MATLAB's `care` function. P Press again K = R -1 FᵀP Calculate the feedback matrix KAnd it's embedded into the controller. In real-vehicle applications, the controller directly calls the pre-stored... K Matrix and Real-time Status x Calculate steering angle δx No online equation solving is required.

[0062] Solving the Riccati equation ensures the feedback matrix K The optimal solution for the quadratic cost function is found, achieving an optimal balance between stability, response speed, and energy consumption in steering control. Based on Lyapunov stability theory, a positive definite matrix is ​​used. P The existence of this ensures that the closed-loop system is globally asymptotically stable in all states, with no local unstable regions. The equations are solved offline and then fixed. K A matrix avoids the controller from performing complex numerical calculations in real-time control, reduces hardware resource consumption, and improves system reliability.

[0063] like Figure 3 As shown, a second aspect of the present invention provides a steering control device, comprising: The calculation module 100 is used to construct the whole vehicle model and calculate the vehicle state in combination with the initial perception parameters of the whole vehicle. The calculation module 200 is used to calculate the preset steering angle of the axle based on a preset objective function; The control module 300 is used to acquire the real-time sensing parameters of the whole vehicle and the real-time steering angle of the axle to form a closed-loop control.

[0064] According to the steering control device provided in the second aspect of the present invention, the device adopts an independent design of three modules: solution, calculation, and control. Each module communicates through a standardized interface, reducing system coupling. When a module fails, basic system functions can be ensured through module-level redundancy switching, improving overall reliability. Simultaneously, the modular structure facilitates future upgrades, reducing maintenance costs. Real-time feedback correction ensures that the deviation between the actual steering angle and the preset value is controlled within a preset angle range; furthermore, the real-time monitoring function can quickly identify abnormal states and trigger safety protection mechanisms, reducing the risk of vehicle loss of control.

[0065] Please continue reading Figure 3 The steering control device provided in the second aspect of the present invention is a functional entity that combines hardware and software to realize the steering control method of the first aspect.

[0066] The solution module 100 is used to integrate the construction of the whole vehicle model and the calculation of vehicle state. The calculation module 200 is used to calculate the optimal preset steering angle of the axle based on the preset objective function and vehicle state parameters. The control module 300 realizes closed-loop control adjustment through real-time sensing data and feedback of the actual steering angle.

[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A steering control method, characterized in that, include: Construct a vehicle model and calculate the vehicle state by combining the initial perception parameters of the vehicle; The preset steering angle of the axle is calculated based on a preset objective function; The system acquires real-time sensing parameters of the entire vehicle and real-time steering angles of the axles to form a closed-loop control.

2. The steering control method according to claim 1, characterized in that, In the step of constructing the complete vehicle model The vehicle model is simplified into a six-degree-of-freedom articulated vehicle model; The six-degree-of-freedom model of the articulated vehicle is constructed using the following formula: ； Among them, state variables x =[ β1 γ1 γ2 γ3 θ1 θ2 ] T ; System input δ =[ δ1 δ2 δ3 δ4 δ5 δ6 ] T .

3. The steering control method according to claim 2, characterized in that, The state quantity x middle, β The sideslip angle is the center of gravity of the vehicle body. γ Let be the yaw rate of the vehicle body, and be the hinge angle between adjacent vehicle bodies.

4. The steering control method according to claim 2, characterized in that, The system's input δ middle, δ1 to δ6 The hinge angle between vehicle units.

5. The steering control method according to any one of claims 1 to 4, characterized in that, In the step of constructing a vehicle model and calculating the vehicle state by combining the initial perception parameters of the vehicle, The initial sensing parameters include the vehicle's center of gravity sideslip angle. β Hinge angle between adjacent vehicle bodies θ and the steering angle of the non-driver operating axis δ .

6. The steering control method according to claim 5, characterized in that, The preset objective function is a cost function, and the expression of the cost function is: ； in, β1 , β2 , β3 The sideslip angles are the centers of gravity of the three vehicle bodies. θ1 , θ2 The hinge angle between adjacent vehicle bodies. δx This refers to the steering angle of the non-driver operated axis.

7. The steering control method according to claim 6, characterized in that, The quadratic form matrix of the cost function is: ； in, It is a symmetric matrix. It is a unit array.

8. The steering control method according to claim 7, characterized in that, The control feedback quantity is solved based on the cost function in the form of a quadratic matrix. δx ,set up δx =- Kx ,in K 5×6 For the feedback matrix, x =[ β1 γ1 γ2 γ3 θ1 θ2 ] T It is a state variable.

9. The steering control method according to claim 8, characterized in that, The feedback matrix K By solving the Riccati equation, we obtain the following: ； in, P 6×6 Let be a solution to the Ricardi equation, and let the feedback matrix be... K = R -1 F T P The steering angle of the non-driver operating shaft is: 。 10. A steering control device, characterized in that, include: The solution module is used to construct the vehicle model and solve the vehicle state by combining the initial perception parameters of the vehicle. The calculation module is used to calculate the preset steering angle of the axle based on a preset objective function; The control module is used to acquire the real-time sensing parameters of the whole vehicle and the real-time steering angle of the axle to form a closed-loop control.

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