Control method, device and equipment for torque tracking and storage medium

Abstract

The invention discloses a control method, device and equipment for torque tracking and a storage medium, and the method comprises the steps that a steering system mathematical model corresponding to an electric power steering system is constructed, and the steering system mathematical model comprises an upper steering column and steering wheel differential equation and a lower steering column differential equation; determining a state-space equation corresponding to the electric power steering system based on the steering system mathematical model, and determining optimal state estimation corresponding to the electric power steering system based on the state-space equation and a Kalman filter; and calculating a feedback gain through a linear quadratic regulator, determining a target control quantity based on the optimal state estimation and the feedback gain, and controlling the motor torque of the electric power steering system according to the target control quantity. Quicker dynamic response of the steering system can be improved, the steering accuracy is improved, the steering hand feeling of a driver can be improved, and the driving pleasure is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power steering systems, and particularly to a control method, device, equipment and storage medium for torque tracking. Background Art

[0002] As an important part of vehicle lateral control, the performance of EPS (Electric Power Steering System) directly affects the handling, safety and driver comfort of the vehicle. Therefore, the development of the steering system is particularly important. It not only needs to adapt to the challenges brought by current vehicle intelligence, but also to the higher safety and performance requirements brought by future fully autonomous driving.

[0003] Most of the existing technologies adopt algorithms such as PI (Proportional Integral Control) and LQR (Linear Quadratic Regulator) for tracking control. The PI control structure is simple and suitable for single-input single-output systems, and the problem of integral saturation is also relatively obvious. When the integrator saturates, the recovery response speed drops significantly. In addition, the absence of the differential (D) link causes the system to be unable to predict the change trend of the error, and overshoot or extended adjustment time is likely to occur when a sudden load or a set value mutation occurs.

[0004] The LQR has a relatively high computational complexity. However, when the LQR algorithm is designed, system noise and measurement noise are not considered. It mainly derives through an idealized model and lacks a noise suppression mechanism. Moreover, the LQR requires that all state variables need to be directly measured by sensors. Some state variables need to add new sensors or design additional state observers, otherwise it cannot be directly applied, which limits the application of the algorithm and may increase the material cost. Summary of the Invention

[0005] The present invention provides a control method, device, equipment and storage medium for torque tracking, so as to improve the faster dynamic response of the steering system, improve the steering accuracy and also enhance the driver's steering feel and driving pleasure.

[0006] According to one aspect of the present invention, a control method for torque tracking is provided, including:

[0007] Construct a steering system mathematical model corresponding to the electric power steering system, wherein the steering system mathematical model includes the upper steering column and the steering wheel differential equation and the lower steering column differential equation;

[0008] Determine the state space equation corresponding to the electric power steering system based on the steering system mathematical model, and determine the optimal state estimate corresponding to the electric power steering system based on the state space equation and the Kalman filter;

[0009] The feedback gain is calculated by a linear quadratic regulator, the target control amount is determined based on the optimal state estimation and the feedback gain, and the motor torque of the electric power steering system is controlled according to the target control amount.

[0010] According to another aspect of the present invention, there is provided a control device for torque tracking, including:

[0011] A model construction module that constructs a steering system mathematical model corresponding to the electric power steering system, wherein the steering system mathematical model includes an upper steering column and a steering wheel differential equation and a lower steering column differential equation;

[0012] A state estimation module, configured to determine a state space equation corresponding to the electric power steering system based on the steering system mathematical model, and determine an optimal state estimation corresponding to the electric power steering system based on the state space equation and a Kalman filter;

[0013] A control amount determination module, configured to calculate a feedback gain by a linear quadratic regulator, determine a target control amount based on the optimal state estimation and the feedback gain, and control the motor torque of the electric power steering system according to the target control amount.

[0014] According to another aspect of the present invention, there is provided an electronic device, including:

[0015] At least one processor;

[0016] And a memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the control method for torque tracking according to any embodiment of the present invention.

[0018] According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the control method for torque tracking according to any embodiment of the present invention when executed.

[0019] In the technical solution of the embodiment of the present invention, a steering system mathematical model corresponding to an electric power steering system is constructed, wherein the steering system mathematical model includes a differential equation of the upper steering column and the steering wheel and a differential equation of the lower steering column; based on the steering system mathematical model, a state space equation corresponding to the electric power steering system is determined, and based on the state space equation and a Kalman filter, an optimal state estimate corresponding to the electric power steering system is determined; a feedback gain is calculated through a linear quadratic regulator, and a target control amount is determined based on the optimal state estimate and the feedback gain, and the motor torque of the electric power steering system is controlled according to the target control amount. The technical problem of solving the defects of large overshoot, poor disturbance resistance of traditional PI control and dependence on full state measurement and noise sensitivity of LQR control is solved, the dynamic response of the steering system is made faster, the steering accuracy is improved, and the steering feel of the driver can also be improved, enhancing the driving pleasure.

[0020] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings without creative efforts based on these drawings.

[0022] Figure 1 It is a flowchart of a control method for torque tracking provided by an embodiment of the present invention;

[0023] Figure 2 It is a schematic diagram of LQG closed-loop control provided by an embodiment of the present invention;

[0024] Figure 3 It is a schematic structural diagram of a control device for torque tracking provided by an embodiment of the present invention;

[0025] Figure 4 It is a schematic structural diagram of an electronic device for implementing the torque tracking control method of the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] To enable those skilled in the art to better understand the solution of the present invention, the following will clearly and completely describe the technical solution in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0027] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned accompanying drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0028] Figure 1 It is a flowchart of a control method for torque tracking provided by an embodiment of the present invention. This embodiment is applicable to the situation of torque tracking control of an electric power steering system. This method can be executed by a control device for torque tracking. The device can be implemented in the form of hardware and / or software, and the device can be configured in a vehicle. As Figure 1 shown, the method specifically includes the following steps:

[0029] S110. Construct a steering system mathematical model corresponding to the electric power steering system.

[0030] Among them, the steering system mathematical model includes the upper steering column and the differential equations of the steering wheel and the lower steering column differential equation.

[0031] Specifically, in order to more intuitively express the motion relationship of the steering system, the present invention introduces a kinematic model of the steering system. The kinematic differential equation of the steering system is as follows. Among them, formula (1) is the differential equation of the upper steering column and the steering wheel, and formula (2) is the lower steering column differential equation. The upper steering column is the part above the hand force sensor, and the lower steering column includes the lower steering column, the motor and other actuators.

[0032]

[0033] Where: J c is the equivalent moment of inertia of the steering wheel and the upper steering column; θ cis the steering wheel angle (measured by the TAS sensor); T d is the driver's hand force; b c is the equivalent damping coefficient of the steering wheel and the upper steering column; K tas is the Tas torsion bar stiffness coefficient; is the sensor deformation; b tas is the sensor damping coefficient.

[0034]

[0035] Where: J pn is the equivalent moment of inertia of the lower steering column; θ pn is the angle of the lower steering column; T m is the motor torque; b pn is the equivalent damping coefficient of the lower steering column; F fri is the equivalent frictional force; i is the motor reduction ratio.

[0036] S120. Determine the state space equation corresponding to the electric power steering system based on the mathematical model of the steering system, and determine the optimal state estimation corresponding to the electric power steering system based on the state space equation and the Kalman filter.

[0037] In some embodiments, the state space equation includes: state variables, input variables, output variables, system matrix, control matrix, observation matrix, and transfer matrix.

[0038] Specifically, based on the above embodiments, the state space equation can be determined based on the kinematic differential equation of the steering system, where the state variable x is in the following form:

[0039]

[0040] The input variable u is in the following form:

[0041]

[0042] The output variable y is in the following form:

[0043]

[0044] Where T tas is the hand force value measured by the sensor.

[0045] Therefore, the space state equation of the steering system can be obtained as:

[0046]

[0047] y = Cx + Du (4)

[0048] Where the system matrix A is:

[0049]

[0050] The control matrix B is as follows:

[0051]

[0052] The observation matrix C is as follows:

[0053]

[0054] The transfer matrix D is as follows:

[0055]

[0056] The state - space equation is used to accurately describe the dynamic behavior of an LTI (Linear Time - Invariant) system. Through two equations, namely the state equation and the output equation, it perfectly describes the evolution of the system state over time. Converting the differential equation of the steering system into a state - space equation can obtain the system state (such as tas deformation, the angle and speed of the lower pipe column, etc.) through the changes in input quantities (driver's hand torque, system friction, motor torque).

[0057] In some embodiments, the Kalman filter includes a prediction formula and a correction formula. Based on the state - space equation and the Kalman filter, determining the optimal state estimate corresponding to the electric power steering system includes: estimating the state variables corresponding to the state - space equation based on the prediction formula and the correction formula to obtain the optimal state estimate.

[0058] Specifically, the LQE (Kalman filter) algorithm of this embodiment is designed as follows:

[0059] It can be seen from the formula that the factors affecting the estimation accuracy of state variables are relatively complex and are related to the modeling process noise, parameter identification accuracy, and measurement noise. Therefore, we use the method of data fusion to improve the estimation accuracy. The Kalman filter can better balance the influence between prediction and measurement in state estimation.

[0060] The Kalman filter is obtained during the derivation by taking the derivative of the trace of the error covariance matrix. It is mainly used to handle the uncertainty between noisy measurement data and the prediction model to improve the accuracy and stability of the estimation results. The following are the five core formulas of the Kalman filter, where formula (5) and formula (6) are prediction formulas, and formula (7), formula (8), and formula (9) are correction formulas.

[0061]

[0062] Where: is the prior estimate of the state at time k; A is the state matrix (i.e., the system matrix); is the posterior estimate of the state at time k-1; B is the input matrix (i.e., the control matrix); u k-1 is the input at time k-1.

[0063]

[0064] Where: is the prior error covariance matrix at time k; P k-1 is the error covariance matrix at time k-1; V is the covariance matrix of the process noise, which follows a Gaussian distribution.

[0065]

[0066] Where: K k is the Kalman gain; H is the measurement matrix; W is the covariance matrix of the measurement noise, which follows a Gaussian distribution.

[0067]

[0068] Where: is the posterior estimate of the state at time k; Z k is the measurement matrix.

[0069]

[0070] Where: P k is the updated value of the error covariance matrix at time k; I is the identity matrix.

[0071] Therefore, in formula (4), the quantity measured by the sensor can be used as the measurement term, formula (3) can be used as the prediction model, and after discretizing the state space equation of the steering system, combined with the Kalman formula, and calibrated according to the actual working conditions, the process noise matrix and the measurement noise matrix are determined to obtain more accurate state variables.

[0072] S130. Calculate the feedback gain through the linear quadratic regulator, determine the target control quantity based on the optimal state estimate and the feedback gain, and control the motor torque of the electric power steering system according to the target control quantity.

[0073] In some embodiments, the linear quadratic regulator includes a cost function, a feedback gain equation, and a Riccati equation. Calculating the feedback gain through the linear quadratic regulator includes: minimizing the cost function to obtain the state weight matrix and the input weight matrix; substituting the state weight matrix and the input weight matrix into the feedback gain equation and the Riccati equation to obtain the feedback gain.

[0074] Specifically, the linear quadratic regulator is a state feedback controller based on the optimal control theory. It selects the optimal control input by minimizing a quadratic cost function, and its design is based on the cost function, the system state space equation, and the Kalman observer. The cost function of the LQR is in the following form:

[0075]

[0076] where J is the cost function; x is the system state variable; u is the input vector; Q is the state weight matrix, semi - positive definite; R is the input weight matrix, positive definite.

[0077] Generally, the Q matrix and the R matrix are diagonal matrices. The elements on the diagonal correspond to the weight magnitudes of different state variables and input variables. The larger the element, the greater the importance we attach to that quantity during the design, that is, we hope that this quantity remains small during the change process.

[0078] Above, given the system state space equation, by determining the parameters of the Q matrix and the R matrix, the feedback gain can be obtained. The feedback gain equation is in the following form:

[0079] k = R -1 B T P

[0080] where the matrix P is a positive definite matrix representing the weight of the state feedback; the matrix P can be obtained by solving the Riccati equation. After substituting it into the Riccati equation and simplifying, we get:

[0081] A T P + PA - PBR -1 B T P + Q = 0

[0082] By solving through the above process, we can obtain the optimal control quantity. The control quantity u(t) is:

[0083]

[0084] where is the optimal estimated value of the state variable at time t.

[0085] In some embodiments, determining the target control quantity based on the optimal state estimation and the feedback gain includes: determining the target control quantity based on the negative of the product of the optimal state estimation and the feedback gain; wherein, the target control quantity includes the motor torque. That is, the target control quantity is obtained through formula (11).

[0086] The technical solution of the embodiment of the present invention constructs a steering system mathematical model corresponding to the electric power steering system, wherein the steering system mathematical model includes the upper steering column and the differential equations of the steering wheel and the lower steering column; determines the state space equation corresponding to the electric power steering system based on the steering system mathematical model, and determines the optimal state estimation corresponding to the electric power steering system based on the state space equation and the Kalman filter; calculates the feedback gain through the linear quadratic regulator, determines the target control quantity based on the optimal state estimation and the feedback gain, and controls the motor torque of the electric power steering system according to the target control quantity. It solves the technical problems of the large overshoot, poor disturbance resistance of the traditional PI control and the defects of the LQR control depending on the full state measurement and being sensitive to noise, improves the faster dynamic response of the steering system, improves the steering accuracy, and can also improve the driver's steering feel and driving pleasure.

[0087] Figure 2 This is the schematic diagram of the LQG closed-loop control provided by the embodiment of the present invention. As Figure 2 shown, the LQG closed-loop control is an optimal control strategy based on state feedback, which is used to solve the optimal control problem of linear systems in the presence of Gaussian noise. It consists of two parts: the optimal state feedback controller (linear quadratic regulator) and the state observer (Kalman filter). According to the derivation results of the steering system mathematical model, the LQE algorithm, and the LQR algorithm, taking T m as the state variable (the optimal state variable is obtained through the steering system mathematical model and the hand force measured by the sensor passing through the state observer), T tas as the output signal, substituting it into the LQE and LQR algorithms, the optimal control quantity u(t) can be obtained. The control quantity u(t) calculated above is the system control torque obtained, that is, formula (11).

[0088] 1. The present invention performs torque tracking based on the LQG (linear quadratic Gaussian state feedback controller) algorithm, which is applicable to multi-input multi-output systems. LQG combines the optimal state estimation and optimal control, can predict the future dynamics of the system, and corrects the control quantity in advance to reduce overshoot, especially suitable for fast response scenarios.

[0089] 2. The present invention uses the LQG algorithm, introduces system process noise and measurement noise, and effectively separates the measurement noise, process noise and real signal by means of the built-in Kalman filter, can effectively suppress high-frequency noise and random interference, and enhances the tolerance of model uncertainty.

[0090] 3. LQG does not require all state variables of the system to be directly measurable. It estimates the unmeasurable states through an observer, solving the limitation of LQR relying on full-state measurability. In a multi-state variable system, algorithms can be designed for several state variables according to requirements, and the algorithm has good applicability. With the real-time estimation ability of the Kalman filter, the system can adapt to parameter time-varying systems.

[0091] The torque tracking based on the LQG (Linear Quadratic Gaussian state feedback controller) algorithm proposed by the present invention is applicable to multi-input multi-output systems. By combining optimal state estimation and optimal control, it can predict the future dynamics of the system, correct the control quantity in advance to reduce overshoot, and is applicable to fast response scenarios. The algorithm of the present invention estimates the state variables of the system in real time through an embedded Kalman filter, which can effectively separate measurement noise, process noise and the true signal, effectively suppress high-frequency noise and random interference, and enhance the tolerance of model uncertainty. The present invention estimates the state variables based on an observer, does not require all state variables of the system to be directly measurable, solves the limitation of LQR relying on full-state measurability, and in a multi-state variable system, algorithms can be designed for several state variables according to requirements, and the algorithm has good applicability.

[0092] Figure 3 It is a schematic structural diagram of a control device for torque tracking provided by an embodiment of the present invention. As Figure 3 shown, the device includes:

[0093] A model construction module 310 constructs a steering system mathematical model corresponding to the electric power steering system. The steering system mathematical model includes the upper steering column and the steering wheel differential equation and the lower steering column differential equation;

[0094] A state estimation module 320 is used to determine the state space equation corresponding to the electric power steering system based on the steering system mathematical model, and determine the optimal state estimation corresponding to the electric power steering system based on the state space equation and the Kalman filter;

[0095] A control quantity determination module 330 is used to calculate the feedback gain through a linear quadratic regulator, determine the target control quantity based on the optimal state estimation and the feedback gain, and control the motor torque of the electric power steering system according to the target control quantity.

[0096] Optionally, the upper steering column and the steering wheel differential equation are as follows:

[0097]

[0098] where J c is the equivalent moment of inertia of the steering wheel and the upper steering column; θ c is the steering wheel angle (measured by the tas sensor); T dis the driver's manual force; b c is the equivalent damping coefficient of the steering wheel and the upper steering column; K tas is the stiffness coefficient of the Tas torsion bar; is the deformation of the sensor; b tas is the damping coefficient of the sensor.

[0099] Optionally, the differential equation of the lower steering column is as follows:

[0100]

[0101] where J pn is the equivalent moment of inertia of the lower steering column; θ pn is the rotation angle of the lower steering column; T m is the motor torque; b pn is the equivalent damping coefficient of the lower steering column; F fri is the equivalent frictional force; i is the motor reduction ratio.

[0102] Optionally, the state space equation includes: state variables, input variables, output variables, system matrix, control matrix, observation matrix, and transfer matrix.

[0103] Optionally, the state estimation module 320 is specifically used for:

[0104] estimating the state variables corresponding to the state space equation based on the prediction formula and the correction formula to obtain the optimal state estimation.

[0105] Optionally, the linear quadratic regulator includes a cost function, a feedback gain equation, and a Riccati equation. The control quantity determination module 330 is specifically used for:

[0106] minimizing the cost function to obtain the state weight matrix and the input weight matrix;

[0107] substituting the state weight matrix and the input weight matrix into the feedback gain equation and the Riccati equation to obtain the feedback gain.

[0108] Optionally, the control quantity determination module 330 is further used for:

[0109] determining the target control quantity based on the opposite of the product of the optimal state estimation and the feedback gain; where the target control quantity includes the motor torque.

[0110] The control device for torque tracking provided by the embodiments of the present invention can execute the control method for torque tracking provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0111] Figure 4Schematic structural diagram of an electronic device for implementing the torque tracking control method according to an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0112] As Figure 4 shown, the electronic device 10 includes at least one processor 11 and a memory communicatively connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc. The memory stores a computer program executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0113] Multiple components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disc, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0114] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the torque tracking control method.

[0115] In some embodiments, the control method of torque tracking can be implemented as a computer program, which is tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the control method of torque tracking described above can be executed. Alternatively, in other embodiments, the processor 11 can be configured to execute the control method of torque tracking by any other suitable means (e.g., by means of firmware).

[0116] The various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGA), application specific integrated circuits (ASIC), application specific standard products (ASSP), systems on a chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs, which can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special or general programmable processor, and can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0117] The computer programs for implementing the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, a special purpose computer, or other programmable data processing device, such that when the computer programs are executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer programs can be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0118] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0119] To provide for interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can also be used to provide for interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, speech input, or tactile input).

[0120] The systems and techniques described herein can be implemented in a computing system that includes backend components (such as, for example, a data server), or a computing system that includes middleware components (such as, for example, an application server), or a computing system that includes frontend components (such as, for example, a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (such as, for example, a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.

[0121] A computing system may include a client and a server. The client and the server are generally far from each other and usually interact via a communication network. The client-server relationship is created by computer programs that run on respective computers and have a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, solving the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0122] It should be understood that various forms of the processes shown above can be used, steps can be reordered, added, or deleted. For example, the steps described in the present invention can be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and no limitations are imposed herein.

[0123] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A control method for torque tracking, characterized in that, including: Construct a steering system mathematical model corresponding to the electric power steering system, wherein the steering system mathematical model includes the upper steering column and the differential equations of the steering wheel and the lower steering column; Based on the steering system mathematical model, determine the state space equation corresponding to the electric power steering system, and based on the state space equation and the Kalman filter, determine the optimal state estimate corresponding to the electric power steering system; Calculate the feedback gain through a linear quadratic regulator, and based on the optimal state estimate and the feedback gain, determine the target control quantity, and control the motor torque of the electric power steering system according to the target control quantity.

2. The method according to claim 1, wherein The differential equation of the upper steering column and the steering wheel is as follows: Among them, J c is the equivalent moment of inertia of the steering wheel and the upper steering column; θ c is the steering wheel angle (measured by the tas sensor); T d is the driver's hand force; b c is the equivalent damping coefficient of the steering wheel and the upper steering column; K tas is the Tas torsion bar stiffness coefficient; is the deformation of the sensor; b tas is the damping coefficient of the sensor.

3. The method according to claim 2, wherein The differential equation of the lower steering column is as follows: Among them, J pn is the equivalent moment of inertia of the lower steering column; θ pn is the angle of rotation of the lower steering column; T m is the motor torque; b pn is the equivalent damping coefficient of the lower steering column; F fri is the equivalent frictional force; i is the motor reduction ratio.

4. The method according to claim 1, wherein The state space equation includes: state quantity, input quantity, output quantity, system matrix, control matrix, observation matrix and transfer matrix.

5. The method according to claim 1, characterized in that, The Kalman filter includes a prediction formula and a correction formula. Determining the optimal state estimate corresponding to the electric power steering system based on the state space equation and the Kalman filter includes: Estimate the state quantity corresponding to the state space equation based on the prediction formula and the correction formula to obtain the optimal state estimate.

6. The method according to claim 5, wherein The linear quadratic regulator includes a cost function, a feedback gain equation and a Riccati equation. Calculating the feedback gain through the linear quadratic regulator includes: Minimize the cost function to obtain the state weight matrix and the input weight matrix; Substitute the state weight matrix and the input weight matrix into the feedback gain equation and the Riccati equation to obtain the feedback gain.

7. The method according to claim 1, wherein Determining the target control quantity based on the optimal state estimate and the feedback gain includes: Determine the target control quantity based on the negative value of the product of the optimal state estimate and the feedback gain; wherein the target control quantity includes the motor torque.

8. A control device for torque tracking, characterized in that, including: A model construction module that constructs a steering system mathematical model corresponding to the electric power steering system, wherein the steering system mathematical model includes the differential equations of the upper steering column and the steering wheel and the lower steering column; A state estimation module for determining the state space equation corresponding to the electric power steering system based on the steering system mathematical model, and determining the optimal state estimate corresponding to the electric power steering system based on the state space equation and the Kalman filter; A control quantity determination module for calculating the feedback gain through a linear quadratic regulator, determining the target control quantity based on the optimal state estimate and the feedback gain, and controlling the motor torque of the electric power steering system according to the target control quantity.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; And a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the control method for torque tracking according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a processor to implement the control method for torque tracking according to any one of claims 1-7 when executed.

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