Torque closed-loop control method for steer-by-wire control system

Abstract

The present invention relates to the technical field of automotive wire-controlled steering, and proposes a torque closed-loop control method for a wire-controlled steering control system. First, the steering wheel assembly is simplified into a two-degree-of-freedom model, and a dynamic model of the upper steering system is constructed based on this model. Subsequently, the angles and angular velocities of the upper and lower ends of the torsion bar are selected as state quantities, and a state space equation is established. In order to accurately observe the system state, an LQE optimal observer is designed to observe the optimal state of the system. According to the observed state, combined with the desired torque, the TAS torque is selected as the output signal, and an LQR controller is designed to calculate the gain matrix, and the required control torque is obtained accordingly. The entire control process forms a closed loop to ensure system stability and accuracy. In addition, the present invention also provides a wire-controlled steering control system for implementing the control method, which integrates a sensor, an observer, a controller and an actuator, as well as a feedback mechanism, thereby achieving efficient and precise steering control.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile steer-by-wire technology, and in particular to a torque closed-loop control method of a steer-by-wire control system. Background Art

[0002] With the continuous development of intelligent technology, the application of chassis-by-wire technology in the automotive industry has gradually attracted widespread attention and adoption. As a key part of chassis-by-wire, steering-by-wire (SBW) is crucial to the intelligent transformation of automobiles.

[0003] The Steering-By-Wire system removes the mechanical connection between the steering wheel and the steering wheel in the traditional steering system, completely gets rid of the limitations of mechanical firmware, and relies on sensors and bus technology to achieve accurate processing and high-speed transmission of related signals. This design allows the SBW system to theoretically achieve any steering intention, and is therefore regarded as one of the most advanced steering systems currently available.

[0004] However, the SBW system also faces a series of challenges, one of the most prominent of which is the difficulty in simulating the road feel and hand feel. Due to the elimination of mechanical connection, it is difficult for the SBW system to provide the driver with the same road feel and hand feel feedback as the traditional mechanical steering system.

[0005] In particular, most of the existing steer-by-wire control systems adopt a torque open-loop control mode, that is, the expected torque of the upper module is directly sent to the motor for execution. However, due to various uncertain factors (such as inaccurate system model parameters, external interference, and sensor noise), the actual output torque of the motor often deviates from the driver's expected torque, resulting in a deviation between the actual steering wheel torque and the driver's expected steering torque. It is impossible to accurately follow the driver's steering intention, which seriously affects the driving experience and limits the promotion and application of the SBW system.

[0006] In order to solve these problems, especially the problem that the actual steering wheel torque cannot accurately follow the driver's desired torque, the researchers proposed a torque closed-loop control scheme. Summary of the invention

[0007] One object of the present invention is to provide a torque closed-loop control method for a wire-controlled steering control system, which dynamically adjusts the torque by utilizing a method of predicting the current state and controlling the gain, thereby achieving precise control of the actual torque of the steering wheel, so that it can better follow the driver's desired torque, thereby improving driving comfort and safety.

[0008] In order to solve the above technical problems, the present invention provides a torque closed-loop control method for a wire-controlled steering control system, wherein the wire-controlled steering control system includes a steering wheel assembly, which includes a steering wheel, a torsion bar, a road sensing motor and a sensor; wherein the torsion bar includes an upper column and a lower column. An execution assembly is used to drive the wheel to steer according to a received control signal. An optimal control module estimates the optimal state of the system through the system state collected by the sensor, and outputs a control torque to the execution assembly according to the estimation result. The optimal control module includes an LQE optimal observer and an LQR controller. The wire-controlled steering control system also includes a feedback unit, which is used to feed back the actual output of the execution assembly or the system state to the optimal control module to achieve closed-loop control.

[0009] The control method comprises the following steps:

[0010] The steering wheel assembly is simplified into a two-degree-of-freedom model, and the dynamic model of the steering system on the SBW is established based on the two-degree-of-freedom model;

[0011] The angles and angular velocities of the upper and lower columns of the torsion bar are selected as state quantities, and the state space equation is established;

[0012] Design an optimal estimation module to observe the optimal state of the system; based on the optimal state measured by the optimal estimation module and the expected torque, select the TAS torque as the output signal;

[0013] Design the optimal control module, calculate the gain matrix according to the control strategy of the optimal control module, and control the system through the series component feedback loop to obtain the optimal control law;

[0014] Using the calculated gain matrix and the optimal control law, the required control torque is calculated to achieve closed-loop control of the steering system on the SBW.

[0015] In some embodiments, the two-degree-of-freedom model includes a torsion bar, a transmission mechanism connected to the torsion bar, and dynamic characteristics of a steering wheel and wheels.

[0016] In some embodiments, the optimal estimation module estimates the following steps:

[0017] Discrete transformation: the state space equation is discretized, and the discrete part can use the Euler method or the zero-order hold method.

[0018] Prediction: predict the current state based on the state and control amount at the previous moment.

[0019] Prediction optimization, by adding the current moment observation value and combining it with the current moment prediction value, obtains the optimal state estimate.

[0020] Through the above steps, the optimal estimation module can perform optimal estimation on the system state.

[0021] In some embodiments, the optimal control module calculates the following steps:

[0022] Standard formula based on optimal control module:

[0023]

[0024] in, is the cost function, which represents the quality of the control effect. is the state variable matrix. Q is a semi-positive definite matrix, which represents the proportion of the corresponding state component in the performance index. is the input control matrix. R is a positive definite matrix, representing the weight matrix of the input quantity.

[0025] Set a first weighted Q matrix of state variables and a second weighted R matrix of control variables;

[0026] The optimal control gain is calculated based on the set first weighted Q matrix and second weighted R matrix.

[0027] In some embodiments, the first weighted Q matrix acts on the optimal state quantity calculated by the optimal estimation module to adjust the proportion of the corresponding state component in the performance indicator.

[0028] The second weighted R matrix acts on the input quantity to adjust its proportion in the input.

[0029] In some embodiments, the calculation formula of the control torque is:

[0030]

[0031] where k c is the gain matrix, and x is the system state vector.

[0032] In some embodiments, the calculation of the control torque takes into account the current state of the system and the desired torque output by adjusting the gain matrix k c to achieve optimal control.

[0033] By adopting the above technical solution, the present invention has at least one of the following beneficial effects:

[0034] 1. Improve driving responsiveness and accuracy

[0035] The present invention can predict the state at the previous moment and observe the current state, and optimize / compensate the input according to the predicted value, adjust the weight so that the actual torque follows the expected torque, and combine the optimal observer and the optimal control module to achieve precise control of the actual torque of the upper steering wheel, which enables the upper steering system to more accurately follow the driver's expected torque and improves the responsiveness and accuracy of driving.

[0036] 2. Optimize system dynamics model

[0037] The present invention divides the upper and lower parts of the column into two-degree-of-freedom models, and at the same time equates the upper column part to the steering wheel, and the lower column, motor, pulley, etc. to the gears. In this way, the amount of calculation is greatly reduced during the modeling process, which improves the calculation speed and efficiency, and helps to more accurately describe the dynamic behavior of the system.

[0038] 3. Achieve optimal state observation

[0039] The prediction state module in the present invention predicts the current state and the error covariance at the current moment based on the state at the previous moment, and optimizes the prediction result by combining the sensor value at the current moment. The sensor signal is used to estimate the optimal state of the system, which helps to improve the estimation accuracy of the system state in the presence of noise and uncertainty, thereby enhancing the robustness of the control.

[0040] 4. Optimize control strategy

[0041] The present invention designs an optimal control module, which calculates the optimal control gain matrix by minimizing the objective function including the weighted square sum of the system state and the control input, and calculates the required control torque in combination with the optimal control law. This optimization control strategy helps to minimize the weighted square sum of the control input and the system state while meeting the system performance requirements, thereby achieving a balance between energy consumption and performance. This design method not only takes into account the current state of the system, but also the expected torque output, making the system more robust in the face of external interference and uncertainty.

[0042] 5. Achieve closed-loop control

[0043] The present invention realizes closed-loop control of the SBW system by designing an optimal observer and an optimal control module, and combining the system state information collected by the sensor, and feeding back the actual output of the actuator or the system state to the optimal observer and the optimal control module through a feedback mechanism. At the same time, the control strategy can be continuously adjusted according to the actual situation to maintain the best control effect, correct the deviation in the control process in time, and improve the control accuracy and stability.

[0044] 6. Improve overall system performance

[0045] The wire-controlled steering control system of the present invention integrates multiple components such as a steering wheel assembly, a torsion bar, a sensor, an optimal observer, an optimal control module and an actuator, and achieves precise control of the steering system through collaborative work. This integrated design helps to improve the overall performance of the system, such as response speed, control accuracy and stability.

[0046] In summary, the torque closed-loop control method of the wire-controlled steering control system and the control system thereof of the present invention significantly improve driving comfort and safety by optimizing the system model, realizing optimal state observation, optimizing the control strategy and realizing closed-loop control, while also improving the overall performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The drawings described herein are used to provide further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0048] Figure 1 It is a flow chart of the torque closed-loop control method of the wire-controlled steering control system of the present invention;

[0049] Figure 2 It is a LQG control principle diagram of the torque closed-loop control method of the wire-controlled steering control system in the present invention;

[0050] Figure 3 This is the LQG optimal control block diagram of the steer-by-wire control system;

[0051] Figure 4 is a schematic diagram of the structure of a steer-by-wire control system;

[0052] Figure 5 It is a comparison diagram of the expected torque and the actual torque of the open loop in the serpentine test of the present invention;

[0053] Figure 6 It is a comparison diagram of the expected torque and actual torque of the LQG closed loop in the serpentine test of the present invention;

[0054] Figure 7 It is a comparison diagram of the expected torque and the actual torque of the open loop in the active self-centering test of the present invention;

[0055] Figure 8 It is a comparison diagram of the expected torque and actual torque of the LQG closed loop in the active self-centering test of the present invention. DETAILED DESCRIPTION

[0056] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. The description of the exemplary embodiments is merely illustrative and is by no means intended to limit the present invention and its application or use. The present invention can be implemented in other different forms and is not limited to the embodiments described herein.

[0057] It should be noted that those skilled in the art explicitly and implicitly understand that the embodiments described in the present invention can be combined with other embodiments without conflict. Unless otherwise defined, the technical terms or scientific terms involved in the present invention should have the common meanings understood by people with ordinary skills in the technical field to which the present invention belongs.

[0058] The words "a", "an", "a", "the" and the like involved in the present invention do not indicate quantity limitation and may indicate singular or plural. The terms "include", "comprises", "has" and any variations thereof involved in the present invention are intended to cover non-exclusive inclusions; the terms "first", "second", "third" and the like involved in the present invention are merely used to distinguish similar objects and do not represent a specific ordering of the objects.

[0059] When it comes to method steps, the sequence illustrated in this document represents an exemplary solution, but does not mean a limitation on the sequence.

[0060] Most of the existing wire-controlled steering control systems adopt a torque open-loop control mode, that is, the expected torque of the upper module is directly sent to the motor for execution. However, due to various uncertain factors, the actual output torque of the motor often deviates from the expected torque of the driver, resulting in a deviation between the actual steering wheel torque and the steering torque expected by the driver, and the problem of being unable to accurately follow the driver's steering intention. The present invention particularly provides a technical solution to the above problem. The technical solution, working principle and technical effect of the present invention are described in detail below in conjunction with specific embodiments.

[0061] Reference Figure 4 As shown, the present invention describes a wire-controlled steering control system, which aims to provide a high-precision and high-responsiveness vehicle steering solution. The wire-controlled steering control system consists of three parts: a steering wheel assembly, a controller (ECU) and a steering execution assembly. The various parts communicate with each other through a CAN bus to ensure real-time data transmission and system coordination, so as to achieve real-time information transmission and accurate execution of control instructions.

[0062] The steering wheel assembly includes a steering wheel, a road sensing motor, sensors (such as a steering angle sensor and a torque sensor), and a torsion bar.

[0063] When the driver turns the steering wheel, the steering angle sensor and torque sensor capture the steering wheel's turning angle and the torque applied by the driver, respectively, and convert this information into electrical signals. The motor is responsible for providing the driver with a sense of touch, simulating the feedback force of a traditional steering system, allowing the driver to feel the resistance and feedback of the steering, thereby enhancing the driving experience.

[0064] The controller is the core component of the system, responsible for receiving signals from the steering wheel assembly and processing these signals to generate control instructions.

[0065] The controller contains multiple upper-level modules, such as the active return module, basic power module and yaw damping module, which calculate the desired torque value based on the current driving conditions and the driver's intention.

[0066] In addition, the controller also integrates the LQE (Linear Quadratic Estimator) optimal observer and the LQR (Linear Quadratic Regulator) controller.

[0067] The LQE optimal observer uses the information provided by the sensor to make an optimal estimate of the system state and provide a basis for the controller's decision. The LQR controller calculates the control torque based on the output of the LQE optimal observer to achieve accurate tracking of the desired torque and optimal control of the system.

[0068] The steering actuator assembly communicates with the steering wheel assembly and controller via CAN signals. It includes an actuator (such as a motor or electric power steering) and a TAS sensor (Torque Angle Sensor, TAS for short).

[0069] The actuator drives the wheels to steer according to the control torque calculated by the LQR controller, achieving precise steering control. The TAS sensor measures the actual torque and provides feedback for closed-loop control.

[0070] As an optimization, in order to achieve more precise control, the steer-by-wire control system also includes a feedback mechanism. This mechanism feeds back the actual output of the actuator or the system state to the LQE optimal observer and the LQR controller to form a closed-loop control system. In this way, the controller can continuously adjust the control instructions according to the difference between the actual output and the expected output of the system, thereby achieving precise control of the steering process.

[0071] In actual applications, when the driver turns the steering wheel, the steering angle sensor and torque sensor capture this action and send relevant information to the controller. The controller calculates the desired torque value based on this information and the current driving conditions (such as vehicle speed, road conditions, etc.). The LQE optimal observer then uses the information provided by the sensor to make an optimal estimate of the system state. Based on this estimate, the LQR controller calculates the control torque and sends it to the steering actuator assembly. The actuator drives the wheels to steer according to the control torque, while the TAS sensor measures the actual torque and feeds it back to the controller to form a closed-loop control.

[0072] Based on the above-mentioned steer-by-wire control system, refer to Figure 1 , Figure 2 As shown, the present invention also describes a torque closed-loop control method for a wire-controlled steering control system, which specifically includes the following steps:

[0073] 1. System Modeling and Simplification

[0074] 1. Simplification of two-degree-of-freedom model

[0075] The steering wheel assembly is simplified into a two-degree-of-freedom model, which includes the torsion bar, the transmission mechanism connected to the torsion bar, and the dynamic characteristics of the steering wheel and the wheel. Based on this model, the dynamic model of the steering system on the SBW is established. This model will consider the angle, angular velocity and its derivative of the upper and lower ends of the torsion bar as state variables.

[0076] Specifically, the upper and lower ends of the torsion bar correspond to the upper column and lower column of the mechanical structure of the steering wheel assembly. The upper column includes the steering wheel and the upper column, which are equivalent to the steering wheel and are represented by the symbol sw; the lower column includes the motor, the pulley, and the lower column, which are equivalent to the pinion and are represented by the symbol pn. The differential equation of the upper column is obtained:

[0077] (1)

[0078] The differential equation of the lower string is:

[0079] (2)

[0080] Through the above steps, the complex steering wheel assembly is simplified into a two-degree-of-freedom model, which not only reduces the complexity of the model, but also retains key dynamic characteristics, such as the dynamic behavior of the torsion bar, transmission mechanism, steering wheel and wheels. This simplification makes the model easier to understand and analyze while ensuring a high degree of accuracy.

[0081] In addition, the interaction relationship between the components in the system is constructed by deriving the differential equations of the upper and lower tubular parts. These equations not only take into account state variables such as angle, angular velocity and its derivatives, but also introduce physical parameters such as damping coefficient and stiffness coefficient, so that the model can more accurately reflect the dynamic response of the actual system.

[0082] Based on the above dynamic equations, the performance of the steering system on the SBW can be comprehensively analyzed and optimized accordingly. For example, by adjusting the model parameters, key performance indicators such as system stability, response speed and accuracy can be significantly improved, providing strong support for improving driving experience and safety.

[0083] 2. Establish the state space equation of the controlled object

[0084] In the wire steering control system, by establishing the state space equation of the controlled object, we can accurately describe the dynamic behavior of the system under specific state variables. This equation not only includes the physical parameters inside the system (such as the equivalent moment of inertia of the steering column and steering wheel, the damping coefficient of the steering column and torsion bar, the stiffness coefficient of the torsion bar, etc.), but also considers the control input (such as the steering torque input by the driver and the motor torque) and the system output (such as the rotation angle and angular velocity of the steering wheel and pinion, and the parameters related to the motor).

[0085] Specifically, the state space equation is constructed by selecting the angle, angular velocity and its derivative of the upper and lower columns of the torsion bar as state variables, combined with the control input and output. The specific construction steps are as follows:

[0086] First, we select the state variable x = and control input u = The state variable x includes the angle and angular velocity of the upper end of the torsion bar and the angle and angular velocity of the lower end, specifically:

[0087]

[0088] The control input u includes the input steering torque on the steering wheel (driver input torque) and the motor torque, specifically:

[0089]

[0090] Output y = , establish the state space equation of the controlled object:

[0091] (3)

[0092] Among them, the A, B, C, D matrices are defined according to the dynamic characteristics of the physical system:

[0093]

[0094]

[0095]

[0096]

[0097] in, - Equivalent moment of inertia of steering column and steering wheel;

[0098] - Steering column damping coefficient;

[0099] - Torsion bar stiffness coefficient;

[0100] - Torsion bar damping coefficient;

[0101] - Steering wheel rotation angle;

[0102] - Steering wheel rotation angular velocity;

[0103] - Angle of rotation of the pinion;

[0104] - Angular velocity of the pinion;

[0105] - Input steering torque on the steering wheel, i.e. driver input torque;

[0106] - Motor torque;

[0107] - Reduction ratio coefficient between motor and pinion;

[0108] - Pinion damping coefficient;

[0109] - Equivalent moment of inertia of the motor and belt to the pinion end.

[0110] The above state-space equation can fully capture the dynamic characteristics of the system under different states by selecting the angle, angular velocity and derivative of the upper and lower columns of the torsion bar as state variables. This enables the controller to more accurately adjust the control strategy according to the current state of the system, thereby improving the control accuracy.

[0111] In addition, the establishment of the state space equation can analyze the stability characteristics of the system and take corresponding measures in the controller design to ensure the stable operation of the system. This helps to reduce the fluctuation and instability of the system under complex working conditions and improve driving safety and comfort.

[0112] The state space equations will describe the dynamic behavior of the system under the state variables and be used for the subsequent controller design (LQR optimal control, LQE optimal observation). These algorithms can comprehensively consider multiple performance indicators of the system (such as response time, overshoot, steady-state error, etc.) to achieve optimization of control performance.

[0113] 2. Design of LQE Observer

[0114] 1. LQE Optimal Observer

[0115] Select the signal that the sensor can collect, such as the output of the angle sensor and the angular velocity sensor, as the output quantity. Design the LQE (Linear Quadratic Estimator) optimal observer to observe the optimal state of the system.

[0116] The LQE optimal observer will use the sensor signals to estimate the optimal state of the system by minimizing the variance of the estimation error, thereby improving the estimation accuracy of the system state in the presence of noise and uncertainty.

[0117] Specifically, since discrete control is usually used in practical applications, we need to discretize the continuous-time state space equation. The main discretization methods include the Euler method and the zero-order hold method. Considering that the zero-order hold method is superior to the Euler method in terms of accuracy and stability, we use the zero-order hold method for discretization:

[0118] (1) System discretization

[0119] The zero-order hold method defines the time interval as , and assume that A(t), B(t), and C(t) remain unchanged during the time interval, and use the solution of the state space equation for calculation. In practical applications, MATLAB commands are often used for processing. The commands are as follows:

[0120]

[0121] sysd represents a discrete system, sysc represents a continuous system, c2d represents a continuous-to-discretization instruction, Ts represents discrete time, and zoh represents a zero-order holder discretization method.

[0122] (2) System equation of noise

[0123] After the system is discretized, the system random input noise w(k) and measurement noise v(k) are added to the state space equation, and the system state equation and measurement equation are transformed into:

[0124]

[0125] In the formula, is the random input noise, is the measurement noise, all of which are zero-mean Gaussian white noise, A is the system matrix, B is the control matrix, C is the output matrix or observation matrix, and D is the direct transfer matrix. is the state quantity at time k, is the state quantity at time k+1, is the system output at time k.

[0126] (3) Implementation of LQE observer

[0127] The implementation of the LQE observer is essentially the application of the Kalman filter, which estimates the state of a linear dynamic system by processing a series of measurement data containing noise and other inaccuracies. The Kalman filter performs state estimation in the following two steps:

[0128] ①Step 1: Prediction

[0129] The error covariance between predicting the current state and predicting the current state is given by:

[0130]

[0131] in, is the prior state estimate at time k, is the posterior state estimate at time k-1, is the state transfer matrix at time k, is the control matrix at time k, is the prior estimated covariance matrix at time k, is the posterior estimated covariance matrix at time k-1, is the process noise covariance matrix at time k.

[0132] The purpose of step 1 is to predict the current state based on the state and control quantity of the previous moment. This predicted value is an estimate because it does not take into account the observed value of the current moment. The error covariance matrix of the predicted value is calculated by the error covariance matrix of the previous moment and the system noise covariance matrix.

[0133] ②Step 2: Update the Kalman gain, current state and error covariance of the current state. The formula is:

[0134]

[0135] in, is the Kalman gain at time k, is the transformation matrix from state variables to measurements (observations) at time k, is the measurement noise covariance matrix at time k, is the measurement value at time k, and I is the identity matrix.

[0136] The purpose of step 2 is to calculate the current state estimate based on the current observations and predictions. This estimate is a more accurate estimate because it takes into account the current observations. The error covariance matrix of the state estimate is calculated using the error covariance matrix calculated in the prediction step, the observation noise covariance matrix, and the Kalman gain.

[0137] Through the above steps, we can realize the LQE optimal observer and estimate the system state with high accuracy.

[0138] In summary, the LQE optimal observer estimates the optimal state of the system by minimizing the variance of the estimation error. In the presence of noise and uncertainty, this method can significantly improve the estimation accuracy of the system state.

[0139] In practical applications, the system is often affected by various noises and uncertainties, such as sensor noise, environmental interference, etc. The LQE observer effectively reduces the impact of these factors on the system state estimation by introducing random input noise and measurement noise into the system and processing them during the state estimation process.

[0140] In addition, through the two main steps of the Kalman filter - prediction and update, the LQE observer can gradually correct the state estimate to make it closer to the true value. The prediction step predicts the current state based on the state and control quantity of the previous moment, while the update step corrects the predicted value based on the observed value at the current moment, thereby obtaining a more accurate state estimate.

[0141] Since controllers in practical applications usually use discrete control, the LQE observer needs to discretize the continuous-time state space equation. Discretization is performed using methods such as the zero-order hold method, ensuring the effective application of the LQE observer in discrete control systems.

[0142] 3. Design of LQR Controller

[0143] like Figure 3 As shown in the figure, the structural block diagram of the LQG controlled SBW system (this structural block diagram is commonly used in modern control theory and is often used together with the state-space equation). According to the optimal state of the system observed by the LQE observer and the expected torque, the torque collected by the TAS (Torque Angle Sensor) sensor is selected as the output signal.

[0144] Design an LQR (Linear Quadratic Regulator) controller and calculate the gain matrix based on the LQR control strategy.

[0145] The LQR controller will calculate the optimal control gain matrix by minimizing the objective function consisting of the weighted sum of squares of the system state and control inputs.

[0146] This objective function will include the weighted matrix Q of the state variables and the weighted matrix R of the control variables. By selecting appropriate Q and R, the optimal control law can be obtained.

[0147] The specific implementation process is as follows:

[0148] 1. Construct the objective function

[0149] The optimal state feedback controller is used to calculate the control input to minimize the objective function. Its design is based on the system's state equation, the objective function, and the state estimate obtained by the state estimator.

[0150] The objective function of LQR is:

[0151]

[0152] in, is the cost function, which represents the quality of the control effect. is the state variable matrix. Q is a semi-positive definite matrix, which represents the proportion of the corresponding state component in the performance index. is the input control matrix. R is a positive definite matrix, representing the weight matrix of the input quantity.

[0153] When designing an LQG controller, by reasonably selecting the weighted matrix Q of the state variables and the weighted matrix R of the control variables, the optimal control law can be obtained, so that the closed-loop control system has a sufficiently good dynamic response. In application, these two parameters can be debugged through calibration to obtain the optimal control law.

[0154] 2. Solve the optimal control gain

[0155] After designing the Q matrix and R matrix, the state feedback control gain of the LQR controller can be obtained Optimal state feedback gain Determined by the following formula:

[0156]

[0157] Where P is obtained by solving the Riccati equation:

[0158]

[0159] Among them, A is the system matrix, B is the control matrix, Q is the semi-positive definite matrix, and R is the positive definite matrix.

[0160] 3. Closed-loop control

[0161] The LQG closed-loop optimal control consists of two parts: the LQE observer and the LQR controller. After obtaining the optimal state quantity of the LQE observer and the optimal control gain of the LQR controller, the system is controlled by constructing a feedback loop in series, and the optimal control law is obtained:

[0162]

[0163] in, It is the optimal state.

[0164] The LQR controller of the present invention calculates the optimal control gain matrix by minimizing an objective function including the weighted square sum of the system state and the control input, which enables the control system to respond in an optimal manner, thereby improving the overall control performance.

[0165] In addition, the dynamic response characteristics of the control system can be adjusted by properly selecting the weighted matrix Q of the state variables and the weighted matrix R of the control variables. The Q matrix represents the proportion of the state variables in the performance index, while the R matrix represents the weight of the input quantity. This flexibility enables the LQR controller to adapt to different control requirements.

[0166] The closed-loop control structure (consisting of the LQE observer and the LQR controller) is designed to ensure the stable operation of the system. The LQE observer provides the optimal estimate of the system state, while the LQR controller calculates the optimal control input based on these estimates, thereby achieving stable control of the system.

[0167] 4. Control torque calculation

[0168] Using the calculated gain matrix and optimal control law, the required control torque T is calculated. ut The calculation formula for the control torque is:

[0169]

[0170] Among them, k c is the gain matrix, and x is the system state vector. This control torque will take into account the current state of the system and the desired torque output by adjusting the gain matrix k c to achieve optimal control.

[0171] 5. System Implementation and Testing

[0172] 1. Steer-by-wire control system

[0173] A steer-by-wire control system is constructed, including a steering wheel assembly, a torsion bar, a sensor, an LQE optimal observer, an LQR controller, and an actuator. The sensor is used to collect system state information, the LQE optimal observer is used to estimate the optimal state of the system, the LQR controller is used to calculate the control torque, and the actuator is used to drive the wheel to steer according to the control torque. In addition, the system also includes a feedback mechanism for feeding back the actual output of the actuator or the system state to the LQE optimal observer and the LQR controller to achieve closed-loop control.

[0174] 2. Experimental verification

[0175] In order to verify the effectiveness of the torque closed-loop control method and control system of the wire-controlled steering control system of the present invention, a series of experiments were conducted. The following is a comparative experiment of actual vehicle test conditions under two working conditions, the serpentine test and the active return test, to analyze and explain the closed-loop control and the open-loop control.

[0176] (1) Working condition 1: serpentine test

[0177] like Figure 5 and Figure 6 As shown in the figure, the expected torque when the driver turns the steering wheel 180° back and forth (snake test) and the actual torque measured by the sensor. The data is collected by INCA software and opened by MDA software. Figure 5 For open-loop control logic, Figure 6 It is the LQG closed-loop control logic.

[0178] From the above comparative experiments, it can be seen that in the experiment of turning the steering wheel 180° back and forth, the maximum difference between the actual torque and the expected torque is 2Nm in open-loop control, and the maximum difference between the actual torque and the expected torque is 0.2Nm in closed-loop control. Moreover, the closed-loop has better tracking performance than the open-loop, and the actual torque is closer to the driver's expected torque, which will be more in line with the driver's expected steering and improve steering comfort and safety.

[0179] (2) Working condition 2: Active return test

[0180] like Figure 7 and Figure 8 As shown in the figure, it is the expected torque and the actual torque measured by the sensor when the driver turns the steering wheel to 360° and then lets go, and the steering wheel automatically returns to the center. The data is collected by INCA software and opened by MDA software. Figure 7 For open-loop control logic, Figure 8 It is the LQG closed-loop control logic.

[0181] It can be seen from the above active self-centering experiment that the maximum difference between the actual torque and the expected torque is 2.2Nm in open-loop control, and the maximum difference between the actual torque and the expected torque is 0.18Nm in closed-loop control. In addition, the closed-loop has better tracking performance than the open-loop, and the actual torque is closer to the driver's expected torque, which will be more in line with the driver's expected steering and improve steering comfort and safety.

[0182] Experimental results show that this method can achieve precise control of the actual torque of the steering wheel, so that it can better follow the driver's desired torque. Compared with traditional control methods, this method improves the responsiveness and accuracy of driving, optimizes the system dynamics model, and realizes the optimal state observation and optimization control strategy. At the same time, the implementation of closed-loop control makes the system more robust in the face of external interference and uncertainty.

[0183] The torque closed-loop control method of the wire-controlled steering control system and the control system thereof of the present invention significantly improve the driving comfort and safety by optimizing the system model, realizing optimal state observation, optimizing the control strategy and realizing closed-loop control, while improving the overall performance of the system.

[0184] In summary, the present invention provides a simplified motion equation for the SBW steer-by-wire system and designs an optimal discrete-time controller for the system model using LQR and LQE techniques, thereby achieving closed-loop control of the actual torque of the TAS and the driver's desired torque, thereby improving the driver's steering feel.

[0185] Compared with open-loop control, the technical solution adopted by the present invention has better followability and robustness, and the driving experience is more in line with the driver's expected feel. The torque followability under open-loop control and LQG closed-loop control is compared and analyzed through actual vehicle driving. Under both working conditions, it is shown that the torque followability of LQG closed-loop control is better than that of open-loop control, which verifies the effectiveness of the control strategy proposed by the present invention.

[0186] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0187] In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the fact that ordinary technicians in the field can implement it; when the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

Claims

1. A torque closed-loop control method for a wire-controlled steering control system, the wire-controlled steering control system comprising a steering wheel assembly, the assembly further comprising a steering wheel, a torsion bar, a motor and a sensor; wherein: The torsion bar includes an upper column and a lower column; an actuator assembly, which is used to drive the wheel to steer according to the received control signal; The optimal control module estimates the optimal state of the system through the system state collected by the sensor, and outputs the control torque to the execution assembly according to the estimation result; the optimal control module includes an LQE optimal observer and an LQR controller; a feedback unit is used to feed back the actual output of the execution assembly or the system state to the optimal control module to realize closed-loop control, characterized in that it includes the following steps: The steering wheel assembly is simplified into a two-degree-of-freedom model, and the dynamic model of the steering system on the SBW is established based on the two-degree-of-freedom model; The angles and angular velocities of the upper and lower columns of the torsion bar are selected as state quantities, and the state space equation is established; Design an optimal estimation module to observe the optimal state of the system; the estimation steps of the optimal estimation module are as follows: Discrete transformation: the state space equation is discretized, and the discrete part can use the Euler method or the zero-order hold method; Prediction: predict the current state based on the state and control amount at the previous moment; Prediction optimization, by adding the current moment observation value and combining it with the current moment prediction value, to obtain the optimal state estimate; Through the above steps, the optimal estimation module can be implemented to optimally estimate the system state; According to the optimal state measured by the optimal estimation module and the expected torque, the TAS torque is selected as the output signal; Design the optimal control module, calculate the gain matrix according to the control strategy of the optimal control module, and control the system through the series component feedback loop to obtain the optimal control law; Using the calculated gain matrix and the optimal control law, the required control torque is calculated to achieve closed-loop control of the steering system on the SBW.

2. The control method according to claim 1, characterized in that: The two-degree-of-freedom model includes a torsion bar, a transmission mechanism connected to the torsion bar, and the dynamic characteristics of a steering wheel and wheels.

3. The control method according to claim 1, characterized in that: The optimal control module calculation steps are as follows: Standard formula based on optimal control module: ； in, is the cost function; is the state variable matrix; Q is a semi-positive definite matrix; is the input control matrix; R is a positive definite matrix; Set a first weighted Q matrix of state variables and a second weighted R matrix of control variables; The optimal control gain is calculated based on the set first weighted Q matrix and second weighted R matrix.

4. The control method according to claim 3, characterized in that: The first weighted Q matrix acts on the optimal state quantity calculated by the optimal estimation module to adjust the proportion of the corresponding state component in the performance index; The second weighted R matrix acts on the input quantity to adjust its proportion in the input.

5. The control method according to claim 1, characterized in that: The calculation formula of the control torque is: ； where k c is the gain matrix, and x is the system state vector.

6. The control method according to claim 5, characterized in that: The calculation of the control torque takes into account the current state of the system and the desired torque output, by adjusting the gain matrix k c to achieve optimal control.

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