Vehicle control method, device and equipment, medium and vehicle

Abstract

The invention discloses a vehicle control method and device, equipment, a medium and a vehicle, and the method comprises the steps: obtaining vehicle state data; estimating a system state by adopting Kalman filtering based on the vehicle state data to obtain an optimal state estimation value; obtaining a feed-forward gain according to the REPS system state-space equation and the motor torque; obtaining a feedback gain according to the REPS system state space equation and the Riccati equation; and determining an optimal control gain according to the feedforward gain and the feedback gain, and obtaining an optimal control law according to the optimal control gain and the optimal state estimation value, so as to control a motor to drive a steering wheel to rotate through the optimal control law. The method can reduce the external interference influence on the system, accelerates the response speed of the system, enables the actual steering wheel rotation angle and rotation speed to closely track the target steering wheel rotation angle and rotation speed, reduces the tracking error, and achieves the stable control.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, specifically to a vehicle control method, device, equipment, medium, and vehicle. Background Technology

[0002] In vehicles, EPS (Electric Power Steering) position servo control operates when the driver is not operating the steering wheel. The EPS controller receives a target steering angle request command from ADAS (Advanced Driver Assistance Systems) and automatically drives the motor to output assist / control torque, causing the steering wheel angle to track the target value. This achieves automatic steering execution and lateral movement control of the vehicle. For example, the Automatic Parking Assist (APA) function.

[0003] Currently, the dual-loop PID control strategy, namely the outer loop position loop and the inner loop speed loop, is used in the process of automatically turning the steering wheel to the angle required by ADAS. This strategy has the following problems: a large number of parameters, which are difficult to tune and adjust; poor control accuracy, as a large gain is usually required to meet high tracking performance, but excessively high gain will bring strong oscillations and overshoot; and it does not provide complete system status information, resulting in insufficient flexibility. Summary of the Invention

[0004] This application provides a vehicle control method, device, equipment, medium, and vehicle to improve control accuracy and flexibility.

[0005] In a first aspect, this application provides a vehicle control method, the method comprising:

[0006] Obtain vehicle status data;

[0007] Based on vehicle state data, Kalman filtering is used to estimate the system state and obtain the optimal state estimate.

[0008] The feedforward gain is obtained based on the state-space equations of the REPS system and the motor torque; and the feedback gain is obtained based on the state-space equations of the REPS system and the Riccati equation.

[0009] The optimal control gain is determined based on the feedforward gain and the feedback gain. Based on the optimal control gain and the optimal state estimate, the optimal control law is obtained, and the motor is controlled to drive the steering wheel to rotate through the optimal control law.

[0010] In some embodiments of this application, the vehicle state data includes the state estimate of the previous moment and the control input;

[0011] Based on vehicle state data, a Kalman filter is used to estimate the system state and obtain the optimal state estimate, including:

[0012] Based on the estimated value and control input from the previous moment, the system state at the current moment is predicted to obtain the predicted state value and the prediction error covariance matrix at the current moment.

[0013] The state prediction value at the current time is corrected based on the measured value at the current time, and the state estimate and estimation error covariance matrix at the current time are obtained. The optimal state estimate is then obtained from the state estimate and estimation error covariance matrix at the current time.

[0014] In some embodiments of this application, the feedforward gain is obtained based on the state-space equations of the REPS system, including:

[0015] The system transfer model with motor torque as input and steering wheel angle and steering wheel speed as output is obtained from the state space equation of the REPS system.

[0016] A preset step torque input is applied to the system transfer model to obtain the step response sequence of steering wheel angle and steering wheel speed;

[0017] Steady-state response values ​​are selected from the step response sequence, and DC gain is obtained based on the steady-state response values. Feedforward gain is then obtained based on the DC gain.

[0018] In some embodiments of this application, the feedback gain is obtained based on the REPS system state-space equation and the Riccati equation, including:

[0019] Obtain the state transition matrix and input matrix from the state-space equations of the REPS system;

[0020] Based on the state transition matrix, input matrix, preset positive semi-definite matrix, and positive definite matrix, solve the Riccati equation to obtain a symmetric positive definite solution;

[0021] The feedback gain is obtained based on the input matrix, the positive definite matrix, and the symmetric positive definite solution.

[0022] In some embodiments of this application, the optimal control gain is determined based on the feedforward gain and the feedback gain, and the optimal control law is obtained based on the optimal control gain and the optimal state estimate, including:

[0023] The feedforward gain is combined with the feedback gain to obtain the optimal control gain;

[0024] The optimal control law is obtained by weighting the optimal control gain and the optimal state estimate.

[0025] In some embodiments of this application, the state-space equations of the REPS system are obtained based on the mechanical equations of the motor, the mechanical equations of the steering wheel, the mechanical equations of the column, the equivalent equations of the flexible components of the motor and the column, and the equivalent equations of the flexible components between the steering wheel and the column.

[0026] Secondly, embodiments of this application also provide a vehicle control device, the device comprising:

[0027] An observer is used to acquire vehicle state data; based on the vehicle state data, a Kalman filter is used to estimate the system state and obtain the optimal state estimate.

[0028] The controller is used to obtain a feedforward gain based on the REPS system state-space equation and the motor torque; and to obtain a feedback gain based on the REPS system state-space equation and the Riccati equation; to determine an optimal control gain based on the feedforward gain and the feedback gain; and to obtain an optimal control law based on the optimal control gain and the optimal state estimate, so as to control the motor to drive the steering wheel to rotate through the optimal control law.

[0029] Thirdly, this application also provides an electronic device, including a memory and a processor; the memory stores a computer program, and the processor is used to run the computer program in the memory to perform the operations in the vehicle control method provided in the first aspect.

[0030] Fourthly, this application also provides a storage medium storing a plurality of instructions adapted for loading by a processor to execute the steps in the vehicle control method provided in the first aspect.

[0031] Fifthly, this application also provides a vehicle that includes at least one of the above-mentioned vehicle control device, electronic device, and storage medium.

[0032] The vehicle control method, apparatus, device, medium, and vehicle provided in this application include a method that uses Kalman filtering to estimate the system state based on vehicle state data, obtaining the optimal state estimate and reducing the interference of external disturbances on the state estimate. Feedforward gain is obtained based on the REPS system state space equation and motor torque; feedback gain is obtained based on the REPS system state space equation and Riccati equation. The feedforward gain compensates for the motor torque in advance based on the static input-output characteristics of the REPS system state space equation, enabling the motor to output accurate torque control in real time, thereby shortening the system's initial response time and improving dynamic response speed. The feedback gain performs closed-loop correction on the actual output based on the system dynamic characteristics and state coupling relationship in the REPS system state space equation, ensuring that the actual steering wheel angle and speed closely track the target steering wheel angle and speed even when disturbances exist. The optimal control gain is determined based on the feedforward and feedback gains, and the optimal control law is obtained based on the optimal control gain and the optimal state estimate, thereby achieving fast, stable, and high-precision tracking control of the steering wheel angle and speed to ADAS target commands. Attached Figure Description

[0033] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is a schematic diagram of the control logic for automatic parking;

[0035] Figure 2 This is a schematic flowchart of the vehicle control method provided in the embodiments of this application;

[0036] Figure 3 This is a schematic diagram of the vehicle control device provided in the embodiments of this application;

[0037] Figure 4 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0038] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0039] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined as "first" or "second" may explicitly or implicitly include one or more features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0040] "A and / or B" includes the following three combinations: A only, B only, and a combination of A and B.

[0041] The use of "applies to" or "configured to" in this application implies open and inclusive language, which does not preclude applicability to or configuration to devices performing additional tasks or steps. Furthermore, the use of "based on" implies openness and inclusivity, because processes, steps, calculations, or other actions "based on" one or more conditions or values ​​may in practice be based on additional conditions or values ​​beyond those conditions.

[0042] In this application, the term "exemplary" is used to mean "used as an example, illustration, or description." Any embodiment described as "exemplary" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use this application. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that this application can be made without using these specific details. In other instances, well-known structures and processes are not described in detail to avoid obscuring the description of this application with unnecessary detail. Therefore, this application is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0043] The control logic of automatic parking is as follows Figure 1 As shown, it includes the following steps:

[0044] 1) The ADAS controller (such as the APA function module) generates the target instructions required for automatic steering control based on the parking trajectory planning results and sends them to the EPS controller. The target instructions include the target steering wheel angle and the target steering wheel speed.

[0045] 2) The EPS controller obtains the actual operating status information of the steering wheel, including the actual steering wheel angle and the actual steering wheel speed; and feeds back the actual steering wheel angle and the actual steering wheel speed to the ADAS controller so that the ADAS controller can make closed-loop adjustments to parking control based on the feedback information.

[0046] 3) The EPS controller calculates the angle deviation based on the target steering wheel angle and the actual steering wheel angle, and calculates the speed deviation based on the target steering wheel speed and the actual steering wheel speed; the EPS controller performs control calculations based on the angle deviation and speed deviation to generate motor control commands.

[0047] 4) The EPS controller sends motor control commands to the motor. The motor outputs corresponding driving torque according to the motor control commands, which drives the steering actuator to move, so that the steering wheel angle tracks the target steering wheel angle and the target steering wheel speed, thereby achieving the goal of automatic steering control during automatic parking.

[0048] 5) Steps 1) to 4) are executed periodically until the actual steering wheel angle reaches the control requirements corresponding to the target steering wheel angle, thereby completing the steering control of the automatic parking assist function.

[0049] Currently, the above-mentioned dual-loop PID control strategy for EPS position servo control has the following problems:

[0050] 1) Both the position loop and the speed loop contain multiple sets of gain (P / I / D) and additional parameters such as filtering, limiting, and anti-saturation, and the parameters are coupled with each other; they need to be calibrated repeatedly under different operating conditions such as vehicle speed, steering load, friction or clearance, and power supply voltage changes, making it difficult to take into account all operating conditions at once.

[0051] 2) To improve angle tracking speed and steady-state error performance, it is often necessary to increase the gain; however, excessive gain will introduce obvious oscillations, overshoot and noise amplification, especially in low-speed parking with large angles, strong friction and return to center conditions, which are more likely to cause shaking and deterioration of comfort.

[0052] 3) Dual-loop PID typically relies on a small number of error signals (including position error and speed error) for control, making it difficult to systematically utilize the complete state of the motor / steering mechanism (such as motor angular velocity, acceleration, disturbance estimation, etc.). It has limited ability to suppress external disturbances (road reaction force, static friction, backlash) and measurement noise, and is not easy to extend to higher-order optimal control or unified parameter tuning framework.

[0053] To address the aforementioned issues, embodiments of this application provide a vehicle control method, apparatus, device, medium, and vehicle. The vehicle control method, apparatus, device, medium, and vehicle provided in this application are described below with reference to the accompanying drawings.

[0054] like Figure 2 As shown in the figure, this application provides a vehicle control method, which includes the following steps:

[0055] S201, Obtain vehicle status data.

[0056] The vehicle status data includes measurement data and control data of the steering system's operating status, such as the actual steering wheel angle, actual steering wheel speed, equivalent motor angle, torsion bar torque, motor current or motor torque command, etc.

[0057] S202, based on vehicle state data, uses Kalman filtering to estimate the system state and obtain the optimal state estimate.

[0058] Kalman filtering is used to fuse the state information from the previous time step, the control input, and the measurement value at the current time step, thereby obtaining a more accurate state estimate in the presence of noise and disturbances.

[0059] S203, based on the state-space equation of the REPS (Rack-drive Electric Power Steering system) system and the motor torque, obtains the feedforward gain; and based on the state-space equation of the REPS system and the Riccati equation, obtains the feedback gain.

[0060] The REPS system state-space equation is a state-space model obtained by modeling the dynamic characteristics of the REPS steering actuator. It is used to characterize the dynamic response relationship between the motor torque input and the outputs such as steering wheel angle and steering wheel speed. The feedforward gain is obtained based on the input-output static gain characteristics in the REPS system state-space equation. This feedforward gain is directly generated from the motor torque and does not depend on error feedback formed by the actual output. Therefore, it can provide drive compensation before the system output completes its dynamic response, reducing the lag effect of feedback correction and thus accelerating the system response speed. The feedback gain is obtained based on the system dynamic characteristics and state coupling characteristics in the REPS system state-space equation. Feedback control continuously minimizes the deviation between the target command and the actual output and suppresses disturbance effects, enabling the actual steering wheel angle and speed to closely track the target steering wheel angle and speed, reducing overshoot and oscillation, and minimizing tracking error.

[0061] S204 determines the optimal control gain based on the feedforward gain and the feedback gain, and obtains the optimal control law based on the optimal control gain and the optimal state estimate, so as to control the motor to drive the steering wheel to rotate, so that the steering wheel angle follows the target steering wheel angle requested by ADAS.

[0062] The vehicle control method provided in this application, based on vehicle state data, uses Kalman filtering to estimate the system state, obtaining the optimal state estimate and reducing the interference of external disturbances on the state estimate. Feedforward gain is obtained based on the REPS system state space equation and motor torque; and feedback gain is obtained based on the REPS system state space equation and Riccati equation. The feedforward gain compensates for the motor torque in advance based on the static input-output characteristics of the REPS system state space equation, enabling the motor to output accurate torque control in real time, thereby shortening the system's initial response time and improving dynamic response speed. The feedback gain performs closed-loop correction on the actual output based on the system dynamic characteristics and state coupling relationship in the REPS system state space equation, ensuring that the actual steering wheel angle and speed closely track the target steering wheel angle and speed even when disturbances exist. The optimal control gain is determined based on the feedforward and feedback gains, and the optimal control law is obtained based on the optimal control gain and the optimal state estimate, thereby achieving fast, stable, and high-precision tracking control of the steering wheel angle and speed to ADAS target commands.

[0063] In some embodiments of this application, vehicle state data includes the state estimate of the previous moment and control inputs.

[0064] Based on vehicle state data, a Kalman filter is used to estimate the system state and obtain the optimal state estimate, including:

[0065] Based on the estimated value and control input from the previous moment, the system state at the current moment is predicted to obtain the predicted state value and the prediction error covariance matrix at the current moment.

[0066] The state prediction value at the current time is corrected based on the measured value at the current time, and the state estimate and estimation error covariance matrix at the current time are obtained. The optimal state estimate is then obtained from the state estimate and estimation error covariance matrix at the current time.

[0067] In some examples, the predicted state at the current time. and the prediction error covariance matrix It can be obtained through the following formula:

[0068]

[0069] In the formula, It is the state prediction at time k based on the optimal state estimate at time k-1; F k It is the state transition matrix at time k, which is also the subsequent state transition matrix A; It is the optimal state estimate at time k-1, which is the subsequent state vector X; B kIt is the control gain matrix at time k, which is the subsequent input matrix B; u k It is the control input at time k; It is the prior error covariance matrix at time k, which is the subsequent symmetric positive definite solution P; Q k It is the process noise covariance matrix at time k; It is the covariance matrix of the posterior estimation error at time k-1.

[0070] The above method uses the state estimate and control input from the previous time step to make a priori prediction of the system state at the current time step, resulting in a predicted state value. Since this predicted value has not yet incorporated the current observations, it is considered a priori estimate. Simultaneously, the prediction error covariance matrix is ​​calculated from the estimated error covariance matrix from the previous time step through state transition propagation and combined with the system process noise covariance matrix; this matrix is ​​used to characterize the uncertainty of the predicted value.

[0071] The correction of the current state prediction value based on the current measurement value is achieved through the following formula:

[0072]

[0073] In the formula, K k H is the Kalman gain matrix at time k. k R is the observation matrix at time k. k It is the measurement noise covariance matrix, z k Let I be the observation vector at time k, and let I be the identity matrix. It is the optimal state estimate at time k. It is the posterior estimation error covariance matrix at time k.

[0074] The aforementioned method corrects and updates the prior prediction based on the deviation between the current observation and the prior prediction, thus obtaining the posterior state estimate for the current moment. Since this posterior state estimate incorporates the observation information from the current moment, it has higher estimation accuracy compared to the prior prediction. Simultaneously, the posterior estimation error covariance matrix is ​​calculated by combining the prior error covariance matrix obtained in the prediction step with the observation noise covariance matrix and updating it using Kalman gain, to characterize the uncertainty of the updated state estimate.

[0075] In some embodiments of this application, the state-space equations of the REPS system include system state terms, input terms, and output terms. The system state terms have a state transition matrix A, the input terms have an input matrix B, and the output terms have output coefficients C. The state transition matrix A characterizes the coupling relationships between the state variables within the REPS system, as well as the influence of parameters such as moment of inertia, damping, stiffness, and load on the system's dynamic characteristics. The input matrix B characterizes the motor torque input T.m The relationship between the derivatives of each state reflects the influence path and intensity of the control input on the system state evolution. The output coefficient C is used to characterize the mapping relationship from the system state variables to the output variable y, that is, it is used to calculate the system output quantity (e.g., motor angular velocity) based on the state vector X. Torsion bar torque Steering wheel angle and steering wheel speed This allows for the characterization and acquisition of the output of the REPS system.

[0076] In some examples, the state vector X includes x1= x2= x3= x4= x5= x6= ,in, It is the motor rotation angle equivalent to that on the rack; It is the equivalent angular velocity of the motor, that is The time derivative; It is the equivalent rotation angle of the coupling; It is the equivalent angular velocity of the coupling, i.e. The time derivative; It is the rotation angle of the torsion bar; It is the angular velocity of the torsion bar, that is The time derivative.

[0077]

[0078] In the formula,

[0079] A= ;

[0080] B= C= ;

[0081] For motor damping, The moment of inertia of the motor. For the rack's equivalent stiffness, For rack-equivalent damping This is the equivalent damping of the rack. The equivalent inertia of the rack is... For load factor, For torsion bar stiffness, For torsion bar damping, For steering wheel damping, The moment of inertia of the steering wheel. This is the motor torque equivalent to the rack.

[0082] The state-space equations of the REPS system are derived from the mechanical equations of the motor, the mechanical equations of the steering wheel, the mechanical equations of the column, the equivalent equations of the flexible components of the motor and the column, and the equivalent equations of the flexible components between the steering wheel and the column.

[0083] Among them, the mechanical equations of the motor are used to characterize the motor rotation angle. The moment of inertia and the inertial torque generated by damping, the damping torque and the motor torque and the torque exerted by the motor on the rack The torque balance relationship between them.

[0084] The mechanical equations of the steering wheel are used to characterize the steering wheel angle. The moment of inertia and the inertial torque generated by damping, the damping torque and the driver's torque and the column torque transmitted by the steering wheel-rack flexible component. The torque balance relationship between them.

[0085] The mechanical equations of the tubular column are used to characterize the equivalent rotation angle of the rack. The moment of inertia and the inertial torque generated by damping, the damping torque and the torque exerted by the motor on the rack Column torque and load factor The torque balance relationship between them.

[0086] The equivalent equations for the flexible components of the motor and the column are used to characterize the torque exerted by the motor on the rack. With motor rotation angle Equivalent rotation angle of rack and pinion The damping equivalent relationship between the relative rotation angle and its rate of change.

[0087] The equivalent equation for the flexible element between the steering wheel and the column is used to characterize the column torque. With steering wheel angle Equivalent rotation angle of rack and pinion The elastic / damping equivalent relationship between the relative torsional angle and its rate of change.

[0088] In some examples, the mechanical equations of the electric motor are:

[0089] ;

[0090] The mechanical equation of the steering wheel is:

[0091] ;

[0092] The mechanical equation for the tubular column is:

[0093] ;

[0094] The equivalent equations for the flexible components of the motor and the tubing are:

[0095] The equivalent equation for the flexible component between the steering wheel and the column is:

[0096] ;

[0097] In the formula, It is the torque exerted by the motor on the rack; It is the driver's torque; It is the tubular torque, that is, the torque collected by the torque angle sensor; It is the equivalent rotation angle of the rack; It is the equivalent angular velocity on the rack side. It is the equivalent angular acceleration on the rack side; It is the rotation angle of the torsion bar; It is the angular acceleration of the motor; It is the angular acceleration of the steering wheel.

[0098] Understandably, by simultaneously solving the mechanical equations of the motor, steering wheel, and column, as well as the equivalent equations of the flexible components between the motor and column, and selecting the motor angle, speed, coupling section angle, speed, torsion bar angle, and speed as state variables, and performing variable substitution, elimination, and matrix transformation on the simultaneous equations, the above-mentioned EPS system state space equations are obtained.

[0099] In some embodiments of this application, since the introduction of feedforward control can compensate for system dynamics in advance, thereby improving response speed, the feedforward gain can be appropriately increased when faster response performance is required. However, excessive feedforward gain will amplify the system's sensitivity to noise and disturbances, easily leading to problems such as increased noise and overshoot, and in severe cases, may even cause uneven execution or stuttering. Therefore, the feedforward gain provided in the embodiments of this application is obtained in the following way:

[0100] From the state-space equations of the REPS system, we obtain the values ​​with motor torque as input and steering wheel angle as input. and steering wheel speed The output system transfer model.

[0101] A preset step torque input is applied to the system transmission model to obtain the step response sequence of steering wheel angle and steering wheel speed.

[0102] Select the steady-state response value statYref from the step response sequence, and obtain the feedforward gain Kd based on the steady-state response value.

[0103] In some examples, Specifically, the Matlab command is: G = ss(A,B,C(3:4,:),0), which means that the state transition matrix A and the input matrix B remain unchanged, and the steering wheel angle in the 3rd row is selected from the output coefficients C. Steering wheel speed up to line 4 The direct transfer matrix is ​​set to 0, thus transforming the state-space equations of the REPS system into a system transfer model. Then, the feedforward gain Kd of the dynamic system's step response is calculated using the `dcgain` or `step` command. Illustratively, the Matlab `step` command is used as follows: `tmpdata = step(G)`, which applies a preset step input to the system transfer model G to obtain the step response sequence `tmpdata`; `statYref = tmpdata(end)`, which selects the end output value from the step response sequence `tmpdata` as the steady-state response value `statYref` to characterize the steady-state response of the system output under the step input; `Kd = -1 / statYref`, which calculates the feedforward gain Kd based on the steady-state response value `statYref`.

[0104] The vehicle control method provided in this application determines the feedforward gain Kd by using the steady-state response value of the step response based on the system transfer model. Since the steady-state response value characterizes the static proportional relationship between the motor torque input and the steering wheel angle or speed output under steady-state conditions, the feedforward gain matches the static input-output characteristics of the controlled object. This avoids overcompensation caused by blindly increasing the feedforward gain using empirical methods, reduces the risk of noise amplification, increased overshoot, and uneven or even stuttering execution, and achieves fast and stable tracking control of the steering wheel angle and steering wheel speed to ADAS target commands under automatic parking assist function.

[0105] In some embodiments of this application, the feedback gain is obtained based on the REPS system state-space equation and the Riccati equation, including:

[0106] Obtain the state transition matrix and input matrix from the state-space equations of the REPS system;

[0107] Based on the state transition matrix, input matrix, preset positive semi-definite matrix, and positive definite matrix, solve the Riccati equation to obtain a symmetric positive definite solution;

[0108] The feedback gain is obtained based on the input matrix, the positive definite matrix, and the symmetric positive definite solution.

[0109] In some examples, according to optimal control theory, to ensure the EPS system has good robust stability, an LQG-based optimal controller is used for control, and the system performance index is taken as... Where J is the cost function, representing the quality of control; X is the state vector matrix; Q is a positive semi-definite matrix, representing the proportion of the corresponding state component in the performance index; u is the input control matrix; and R is a positive definite matrix, representing the weight matrix of the input quantity.

[0110] When designing an LQG controller, the optimal control law can be obtained by appropriately selecting the positive semi-definite matrix Q of the state variables and the positive definite matrix R of the control variables, thus giving the closed-loop control system a sufficiently good dynamic response. Based on the minimum principle, assuming a satisfactory estimate of the state variables can be obtained, an approximately optimal control force can be derived using the separation principle:

[0111]

[0112] In the formula, B is the input matrix mentioned above, and the symmetric positive definite solution P is obtained by solving the following Riccati equation:

[0113] ;

[0114] In the formula, A is the state transition matrix mentioned above.

[0115] In some embodiments of this application, the optimal control gain is determined based on the feedforward gain and the feedback gain, and the optimal control law is obtained based on the optimal control gain and the optimal state estimate, including:

[0116] The feedforward gain is combined with the feedback gain to obtain the optimal control gain. , = [Kd Kp].

[0117] Based on optimal control gain and optimal state estimate The optimal control law is obtained by weighting the results. In some examples, .

[0118] like Figure 3 As shown in the figure, this application embodiment also provides a vehicle control device, which includes an observer and a controller.

[0119] The observer is used to acquire vehicle state data; based on this data, a Kalman filter is used to estimate the system state to obtain the optimal state estimate. Vehicle state data includes motor torque, motor speed, torsion bar torque, etc.

[0120] The controller is used to obtain the feedforward gain based on the REPS system state-space equation and the motor torque; and to obtain the feedback gain based on the REPS system state-space equation and the Riccati equation; to determine the optimal control gain based on the feedforward gain and the feedback gain; and to obtain the optimal control law based on the optimal control gain and the optimal state estimate, so as to control the motor to drive the steering wheel to rotate through the optimal control law.

[0121] The vehicle control device provided in this application embodiment comprises an observer and a controller connected in series to form a closed-loop vehicle control device. The observer estimates the system state based on vehicle state data using Kalman filtering to obtain the optimal state estimate, reducing the interference of external disturbances on the state estimate. The controller obtains a feedforward gain based on the REPS system state-space equation and motor torque; and a feedback gain based on the REPS system state-space equation and Riccati equation. The feedforward gain compensates for the motor torque in advance based on the static input-output characteristics of the REPS system state-space equation, enabling the motor to output accurate torque control in real time, thereby shortening the system's initial response time and improving dynamic response speed. The feedback gain performs closed-loop correction on the actual output based on the system dynamic characteristics and state coupling relationship in the REPS system state-space equation, ensuring that the actual steering wheel angle and speed closely track the target steering wheel angle and speed even when disturbances exist. The controller also determines the optimal control gain based on the feedforward gain and the feedback gain, and obtains the optimal control law based on the optimal control gain and the optimal state estimate, thereby achieving fast, stable and high-precision tracking control of the steering wheel angle and steering wheel speed to the ADAS target command.

[0122] In some embodiments of this application, the vehicle state data includes the state estimate of the previous moment and the control input; the observer is specifically used for:

[0123] Based on the estimated value and control input from the previous moment, the system state at the current moment is predicted to obtain the predicted state value and the prediction error covariance matrix at the current moment.

[0124] The state prediction value at the current time is corrected based on the measured value at the current time, and the state estimate and estimation error covariance matrix at the current time are obtained. The optimal state estimate is then obtained from the state estimate and estimation error covariance matrix at the current time.

[0125] In some embodiments of this application, the controller is specifically used for:

[0126] The system transfer model with motor torque as input and steering wheel angle and steering wheel speed as output is obtained from the state space equation of the REPS system.

[0127] A preset step torque input is applied to the system transfer model to obtain the step response sequence of steering wheel angle and steering wheel speed;

[0128] Steady-state response values ​​are selected from the step response sequence, and DC gain is obtained based on the steady-state response values. Feedforward gain is then obtained based on the DC gain.

[0129] In some embodiments of this application, the controller is specifically used for:

[0130] Obtain the state transition matrix and input matrix from the state-space equations of the REPS system;

[0131] Based on the state transition matrix, input matrix, preset positive semi-definite matrix, and positive definite matrix, solve the Riccati equation to obtain a symmetric positive definite solution;

[0132] The feedback gain is obtained based on the input matrix, the positive definite matrix, and the symmetric positive definite solution.

[0133] In some embodiments of this application, the controller is specifically used for:

[0134] The feedforward gain is combined with the feedback gain to obtain the optimal control gain;

[0135] The optimal control law is obtained by weighting the optimal control gain and the optimal state estimate.

[0136] In some embodiments of this application, the state-space equations of the REPS system are obtained based on the mechanical equations of the motor, the mechanical equations of the steering wheel, the mechanical equations of the column, the equivalent equations of the flexible components of the motor and the column, and the equivalent equations of the flexible components between the steering wheel and the column.

[0137] Based on any of the above embodiments, another embodiment of this application also provides an electronic device, such as... Figure 4 As shown, the electronic device may include a processor 410, a communications interface 420, a memory 430, and a communication bus 440. The processor 410, communications interface 420, and memory 430 communicate with each other via the communication bus 440. The processor 410 can call logical instructions in the memory 430 to execute the aforementioned vehicle control method.

[0138] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0139] On the other hand, embodiments of this application also provide a storage medium storing a plurality of instructions adapted for loading by a processor to execute the vehicle control method provided in the above embodiments.

[0140] On the other hand, this application also provides a vehicle that includes at least one of the vehicle control device, electronic device and storage medium provided in the above embodiments.

[0141] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0142] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0143] The foregoing has provided a detailed description of a vehicle control method, apparatus, device, medium, and vehicle provided in the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A vehicle control method, characterized in that, The method includes: Obtain vehicle status data; Based on the vehicle state data, Kalman filtering is used to estimate the system state and obtain the optimal state estimate. The feedforward gain is obtained based on the REPS system state-space equation and the motor torque; and the feedback gain is obtained based on the REPS system state-space equation and the Riccati equation. The optimal control gain is determined based on the feedforward gain and the feedback gain, and the optimal control law is obtained based on the optimal control gain and the optimal state estimate, so as to control the motor to drive the steering wheel to rotate through the optimal control law.

2. The vehicle control method according to claim 1, characterized in that, The vehicle status data includes the state estimate of the previous moment and the control input; The step of estimating the system state using Kalman filtering based on the vehicle state data to obtain the optimal state estimate includes: Based on the estimated value of the previous moment and the control input, the system state at the current moment is predicted to obtain the predicted state value and the prediction error covariance matrix at the current moment. The state prediction value at the current time is corrected based on the measured value at the current time to obtain the state estimate and the estimation error covariance matrix at the current time, and the optimal state estimate is obtained from the state estimate and the estimation error covariance matrix at the current time.

3. The vehicle control method according to claim 1, characterized in that, The method for obtaining feedforward gain based on the REPS system state-space equations includes: The system transfer model with motor torque as input and steering wheel angle and steering wheel speed as output is obtained from the state space equation of the REPS system. A preset step torque input is applied to the system transmission model to obtain the step response sequence of steering wheel angle and steering wheel speed; A steady-state response value is selected from the step response sequence, and a DC gain is obtained based on the steady-state response value. The feedforward gain is then obtained based on the DC gain.

4. The vehicle control method according to claim 1, characterized in that, The step of obtaining the feedback gain based on the state-space equations and Riccati equations of the REPS system includes: The state transition matrix and input matrix are obtained from the state-space equations of the REPS system. Based on the state transition matrix, the input matrix, the preset positive semi-definite matrix, and the positive definite matrix, the Riccati equation is solved to obtain a symmetric positive definite solution; The feedback gain is obtained based on the input matrix, the positive definite matrix, and the symmetric positive definite solution.

5. The vehicle control method according to claim 1, characterized in that, The step of determining the optimal control gain based on the feedforward gain and the feedback gain, and obtaining the optimal control law based on the optimal control gain and the optimal state estimate, includes: The feedforward gain is combined with the feedback gain to obtain the optimal control gain; The optimal control law is obtained based on the weighted result of the optimal control gain and the optimal state estimate.

6. The vehicle control method according to any one of claims 1 to 5, characterized in that, The state-space equations of the REPS system are derived from the mechanical equations of the motor, the mechanical equations of the steering wheel, the mechanical equations of the column, the equivalent equations of the flexible components of the motor and the column, and the equivalent equations of the flexible components between the steering wheel and the column.

7. A vehicle control device, characterized in that, The device includes: An observer is used to acquire vehicle state data; based on the vehicle state data, a Kalman filter is used to estimate the system state and obtain the optimal state estimate. The controller is used to obtain a feedforward gain based on the REPS system state-space equation and the motor torque; and to obtain a feedback gain based on the REPS system state-space equation and the Riccati equation; to determine an optimal control gain based on the feedforward gain and the feedback gain; and to obtain an optimal control law based on the optimal control gain and the optimal state estimate, so as to control the motor to drive the steering wheel to rotate through the optimal control law.

8. An electronic device, characterized in that, It includes a memory and a processor; the memory stores a computer program, and the processor is used to run the computer program in the memory to perform the steps of the vehicle control method according to any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium stores a plurality of instructions adapted for loading by a processor to execute the steps of the vehicle control method according to any one of claims 1 to 6.

10. A vehicle, characterized in that, Includes at least one of the following: The vehicle control device as described in claim 7; The electronic device as claimed in claim 8; The storage medium as described in claim 9.

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