A model-based vehicle stability control method and system for four-wheel steering

Abstract

The application provides a kind of based on model prediction four-wheel steering vehicle stability control method and system, method includes constructing the four-wheel steering two-degree-of-freedom model of vehicle, state space conversion is carried out to four-wheel steering two-degree-of-freedom model, to obtain state space equation;Trajectory tracking error model is constructed to vehicle, and model predictive controller state variable is determined based on trajectory tracking error model;State space equation is discretized to obtain discrete model, and discrete model and model predictive controller state variable are input into model predictive controller to carry out prediction optimization, to obtain output control quantity;Based on output control quantity, the additional yaw moment required by vehicle is determined by sliding mode control calculation;Based on additional yaw moment, brake torque is determined, the application effectively makes up the deficiency of traditional model prediction control, and the stability and active safety of vehicle driving are significantly improved.

Description

Technical Field

[0001] This invention belongs to the technical field of vehicle control, specifically relating to a vehicle stability control method and system based on model prediction of four-wheel steering. Background Technology

[0002] Because the Trucksim vehicle model is simplified to a certain extent and has inherent differences from the actual system, it faces the problem of model uncertainty in application. Model predictive control predicts future states through rolling optimization; however, errors propagate and accumulate over time, eventually causing the control output to deviate from the actual system response. In contrast, the core idea of ​​sliding mode control is to constrain the system state to a pre-defined sliding surface. Once the system enters the sliding motion phase, its dynamic behavior is determined solely by the sliding surface equations, no longer depending on the system model parameters, thus effectively addressing model uncertainty.

[0003] Furthermore, the control input of sliding mode control typically includes a sign function, which drives the system state trajectory towards the sliding surface through a high-frequency switching mechanism. This switching characteristic gives it fast response and strong robustness, enabling it to suppress uncertainties in the system in real time, thus performing excellently in handling model uncertainties. In the joint control of four-wheel steering and additional yaw moment, sliding mode control can provide a robust complement to model-based control methods such as model prediction, enhancing the overall system stability and control accuracy.

[0004] Existing model predictive control-based joint control methods for four-wheel steering and additional yaw moment typically rely on a single model predictive algorithm to calculate the target steering angle and required additional yaw moment for each wheel. However, due to the use of only one control strategy, it is difficult to avoid the problem of insufficient control accuracy. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a vehicle stability control method and system based on model prediction four-wheel steering, which solves the technical problems in the prior art.

[0006] In a first aspect, the present invention provides the following technical solution: a vehicle stability control method based on model-predicted four-wheel steering, comprising:

[0007] A two-degree-of-freedom model of the vehicle's four-wheel steering is constructed, and the state space transformation is performed on the four-wheel steering two-degree-of-freedom model to obtain the state space equation;

[0008] Construct a trajectory tracking error model for the vehicle, and determine the state variables of the model predictive controller based on the trajectory tracking error model;

[0009] The state-space equations are discretized to obtain a discrete model. The discrete model and the state variables of the model predictive controller are input into the model predictive controller for prediction optimization to obtain the output control quantity.

[0010] Based on the output control quantity, the additional yaw moment required for the vehicle is determined by the sliding mode control calculation.

[0011] The braking torque is determined based on the additional yaw moment.

[0012] Compared to existing technologies, the advantages of this invention are as follows: This invention incorporates the four-wheel steering system into the model predictive control framework, while employing a sliding mode control strategy for the additional yaw moment, fully leveraging the advantages of both control methods. This fusion control method effectively compensates for the shortcomings of traditional methods that rely solely on model predictive control, significantly improving vehicle stability and active safety.

[0013] Preferably, the four-wheel steering two-degree-of-freedom model is as follows:

[0014] ;

[0015] In the formula, These are the total lateral stiffness of the front wheels and the total lateral stiffness of the rear wheels, respectively. The sideslip angle is the angle of the center of mass. For the longitudinal speed of the vehicle, These are the distances from the center of gravity to the front and rear axles, respectively. The yaw rate is angular velocity. These are the front wheel steering angle and the rear wheel steering angle, respectively. For vehicle weight, The first derivative of the centroid sideslip angle. For rotational inertia, It is the first derivative of the yaw rate.

[0016] Preferably, the state-space equation is:

[0017] ; ;

[0018] ;

[0019] In the formula, State vector The first derivative, These are the system matrix and the input matrix, respectively. To control the input vector.

[0020] Preferably, the trajectory tracking error model is as follows:

[0021] ;

[0022] ;

[0023] ;

[0024] ;

[0025] ;

[0026] ;

[0027] In the formula, Vehicle lateral speed The first derivative, , They are respectively the yaw angles The first derivative and the second derivative, , These are the x-coordinate distances of the vehicle in the global coordinate system. Distance on the vertical axis The first derivative.

[0028] Preferably, the model predicts the controller state variables, including lateral velocity deviation. Heading angle deviation lateral speed of vehicles and yaw rate :

[0029] ; ;

[0030] ; ;

[0031] In the formula, These are the yaw angle and the reference yaw angle, respectively. For the longitudinal speed of the vehicle, The sideslip angle is the angle of the center of mass. For the lateral angle The first derivative.

[0032] Preferably, the discrete model is:

[0033] ; ;

[0034] ;

[0035] ;

[0036] ;

[0037] In the formula, In discrete time steps , The state vector, , , These are the discrete state matrix, discrete input matrix, and output matrix, respectively. , In discrete time steps The system outputs and control inputs, These are the system matrix and the input matrix, respectively. For discrete sampling time, Discrete time steps The sideslip angle and yaw rate of the center of mass. In discrete time steps , The state increment, To be in discrete time steps The control input increment, To be in discrete time steps The control input, It is an identity matrix.

[0038] Preferably, the step of inputting the discrete model and the state variables of the model predictive controller into the model predictive controller for prediction optimization to obtain the output control quantity includes:

[0039] The discrete model and the state variables of the model predictive controller are input into the model predictive controller to construct new state variables. :

[0040] ;

[0041] In the formula, To be in discrete time steps Control input;

[0042] Based on the new state variables Determine at discrete time step New state variables :

[0043] ;

[0044] ;

[0045] In the formula, These are the expanded system matrix and the expanded second input matrix, respectively.

[0046] The new state variables for subsequent discrete time steps are predicted to obtain the prediction equation:

[0047] ;

[0048] In the formula, These are the control time domain and the prediction time domain, respectively. This is the noise matrix;

[0049] Determine the objective optimization function :

[0050] ;

[0051] ; ;

[0052] ;

[0053] In the formula, , These are the state weighting matrix, control weighting matrix, and incremental control matrix, respectively. These are the reference values ​​for the predicted yaw rate and the expected yaw rate, respectively. These are the reference values ​​for the predicted and expected centroid sideslip angles, respectively. In discrete time steps Rear wheel steering angle, center of gravity sideslip angle, To be in discrete time steps The increment of the rear wheel steering angle, These are the maximum rear wheel steering angle, the maximum change in rear wheel steering angle, and the maximum center of gravity sideslip angle, respectively.

[0054] The objective optimization function and the prediction equation are combined to solve for the output control quantity, wherein the output control quantity is the rear wheel steering angle. ;

[0055] ;

[0056] In the formula, These are the total lateral stiffness of the front wheels and the total lateral stiffness of the rear wheels, respectively. The sideslip angle is the angle of the center of mass. For the longitudinal speed of the vehicle, These are the distances from the center of gravity to the front and rear axles, respectively. The yaw rate is angular velocity. For the front wheel steering angle, For vehicle weight, These are the parameters of the first sliding surface. To determine the desired centroid sideslip angle, The coefficient of the first discontinuous term. The sliding surface is the side slip angle of the centroid.

[0057] Preferably, the additional yaw moment for:

[0058] ;

[0059] In the formula, These are the total lateral stiffness of the front wheels and the total lateral stiffness of the rear wheels, respectively. The sideslip angle is the angle of the center of mass. For the longitudinal speed of the vehicle, These are the distances from the center of gravity to the front and rear axles, respectively. The yaw rate is angular velocity. These are the front wheel steering angle and the rear wheel steering angle, respectively. For the parameters of the second sliding surface, For the desired yaw rate, The coefficient of the second discontinuous term. The sliding surface is the yaw rate. Let be the moment of inertia.

[0060] Preferably, the braking torque includes the front wheel braking torque. With rear wheel braking torque :

[0061] ; ;

[0062] ;

[0063] In the formula, The braking forces are for the front and rear wheels, respectively. The vertical loads are for the front and rear wheels, respectively. These are the track widths of the front and rear wheels, respectively. For the wheel radius, To add yaw moment.

[0064] Secondly, the present invention provides the following technical solution: a vehicle stability control system based on model-predictive four-wheel steering, the system comprising:

[0065] The equation module is used to construct a two-degree-of-freedom model of the vehicle's four-wheel steering, and to perform a state-space transformation on the two-degree-of-freedom model of the four-wheel steering to obtain the state-space equations.

[0066] The variable module is used to construct the vehicle's trajectory tracking error model and determine the state variables of the model predictive controller based on the trajectory tracking error model.

[0067] The discrete module is used to discretize the state-space equations to obtain a discrete model. The discrete model and the state variables of the model predictive controller are input into the model predictive controller for prediction optimization to obtain the output control quantity.

[0068] An additional module is used to determine the additional yaw moment required for the vehicle in sliding mode control calculations based on the output control quantity.

[0069] A torque module is used to determine the braking torque based on the additional yaw moment.

[0070] Thirdly, the present invention provides the following technical solution: a computer, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the vehicle stability control method based on model prediction four-wheel steering as described above.

[0071] Fourthly, the present invention provides the following technical solution: a storage medium storing a computer program, wherein when the computer program is executed by a processor, it implements the vehicle stability control method based on model prediction four-wheel steering as described above. Attached Figure Description

[0072] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0073] Figure 1 A flowchart of a model-predictive four-wheel steering-based vehicle stability control method provided in Embodiment 1 of the present invention;

[0074] Figure 2 The simulation results of the present invention are shown in the figure.

[0075] Figure 3 This is a structural block diagram of the vehicle stability control system based on model prediction four-wheel steering provided in Embodiment 2 of the present invention;

[0076] Figure 4 This is a schematic diagram of the hardware structure of a computer provided for another embodiment of the present invention.

[0077] The embodiments of the present invention will be further described below with reference to the accompanying drawings. Detailed Implementation

[0078] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain embodiments of the present invention, and should not be construed as limiting the present invention.

[0079] Example 1

[0080] In Embodiment 1 of the present invention, as Figure 1 As shown, a vehicle stability control method based on model-predicted four-wheel steering includes:

[0081] S1. Construct a two-degree-of-freedom model of the vehicle's four-wheel steering, and perform a state-space transformation on the two-degree-of-freedom model of the four-wheel steering to obtain the state-space equations;

[0082] The four-wheel steering two-degree-of-freedom model is as follows:

[0083] ;

[0084] In the formula, These are the total lateral stiffness of the front wheels and the total lateral stiffness of the rear wheels, respectively. The sideslip angle is the angle of the center of mass. For the longitudinal speed of the vehicle, These are the distances from the center of gravity to the front and rear axles, respectively. The yaw rate is angular velocity. These are the front wheel steering angle and the rear wheel steering angle, respectively. For vehicle weight, The first derivative of the centroid sideslip angle. For rotational inertia, The first derivative of the yaw rate;

[0085] The state-space equation is as follows:

[0086] ; ;

[0087] ;

[0088] In the formula, State vector The first derivative, These are the system matrix and the input matrix, respectively. To control the input vector.

[0089] S2. Construct a trajectory tracking error model for the vehicle, and determine the state variables of the model predictive controller based on the trajectory tracking error model;

[0090] The trajectory tracking error model is as follows:

[0091] ;

[0092] ;

[0093] ;

[0094] ;

[0095] ;

[0096] ;

[0097] In the formula, Vehicle lateral speed The first derivative, , They are respectively the yaw angles The first derivative and the second derivative, , These are the x-coordinate distances of the vehicle in the global coordinate system. Distance on the vertical axis The first derivative.

[0098] The model predicts the controller state variables, including lateral velocity deviation. Heading angle deviation lateral speed of vehicles and yaw rate :

[0099] ; ;

[0100] ; ;

[0101] In the formula, These are the yaw angle and the reference yaw angle, respectively. For the longitudinal speed of the vehicle, The sideslip angle is the angle of the center of mass. For the lateral angle The first derivative.

[0102] S3. Discretize the state space equation to obtain a discrete model. Input the discrete model and the state variables of the model predictive controller into the model predictive controller for prediction optimization to obtain the output control quantity.

[0103] The discrete model is as follows:

[0104] ; ;

[0105] ;

[0106] ;

[0107] ;

[0108] In the formula, In discrete time steps , The state vector, , , These are the discrete state matrix, discrete input matrix, and output matrix, respectively. , In discrete time steps The system outputs and control inputs, These are the system matrix and the input matrix, respectively. For discrete sampling time, Discrete time steps The sideslip angle and yaw rate of the center of mass. In discrete time steps , The state increment, To be in discrete time steps The control input increment, To be in discrete time steps The control input, It is the identity matrix;

[0109] Specifically, the identity matrix here is a 2×2 identity matrix. At the same time, the sampling increment form of this application is used for prediction, which has an integral effect and can eliminate steady-state error. By directly optimizing the control increment instead of the absolute control quantity, the control action is smoother and more robust to model uncertainty and external disturbances.

[0110] Step S3 includes:

[0111] S31. Input the discrete model and the state variables of the model predictive controller into the model predictive controller to construct new state variables. :

[0112] ;

[0113] In the formula, To be in discrete time steps Control input.

[0114] S33, Based on the new state variable Determine at discrete time step New state variables :

[0115] ;

[0116] ;

[0117] In the formula, These are the expanded system matrix and the expanded second input matrix, respectively.

[0118] S33. Predict the new state variables for subsequent discrete time steps to obtain the prediction equation:

[0119] ;

[0120] In the formula, These are the control time domain and the prediction time domain, respectively. This is the noise matrix;

[0121] For the prediction equation, the output variables at the future prediction time are:

[0122] ; ; ;

[0123] ;

[0124] ;

[0125] In the formula, Indicates from discrete time step The predicted output, This is the mapping matrix from state to output. To start from discrete time steps The control input vector, This is the input-to-output mapping matrix. The mapping matrix that interferes with the output. , This is the expanded output matrix.

[0126] S34. Determine the objective optimization function :

[0127] ;

[0128] ; ;

[0129] ;

[0130] In the formula, , These are the state weighting matrix, control weighting matrix, and incremental control matrix, respectively. These are the reference values ​​for the predicted yaw rate and the expected yaw rate, respectively. These are the reference values ​​for the predicted and expected centroid sideslip angles, respectively. In discrete time steps Rear wheel steering angle, center of gravity sideslip angle, To be in discrete time steps The increment of the rear wheel steering angle, These are the maximum rear wheel steering angle, the maximum change in rear wheel steering angle, and the maximum center of gravity sideslip angle, respectively.

[0131] S35. Solve the objective optimization function and the prediction equation to obtain the output control quantity, wherein the output control quantity is the rear wheel steering angle. ;

[0132] ;

[0133] In the formula, These are the total lateral stiffness of the front wheels and the total lateral stiffness of the rear wheels, respectively. The sideslip angle is the angle of the center of mass. For the longitudinal speed of the vehicle, These are the distances from the center of gravity to the front and rear axles, respectively. The yaw rate is angular velocity. For the front wheel steering angle, For vehicle weight, These are the parameters of the first sliding surface. To determine the desired centroid sideslip angle, The coefficient of the first discontinuous term. The sliding surface is the side slip angle of the centroid;

[0134] Specifically, by combining the objective optimization function and the prediction equation, all state variables and control variables in the prediction time domain are determined. Then, the optimal value of the output in the prediction equation is determined through the objective optimization function, thereby determining the rear wheel steering angle.

[0135] S4. Based on the output control quantity, determine the additional yaw moment required for the vehicle using sliding mode control;

[0136] Among them, the additional yaw moment for:

[0137] ;

[0138] In the formula, These are the total lateral stiffness of the front wheels and the total lateral stiffness of the rear wheels, respectively. The sideslip angle is the angle of the center of mass. For the longitudinal speed of the vehicle, These are the distances from the center of gravity to the front and rear axles, respectively. The yaw rate is angular velocity. These are the front wheel steering angle and the rear wheel steering angle, respectively. For the parameters of the second sliding surface, For the desired yaw rate, The coefficient of the second discontinuous term. The sliding surface is the yaw rate. Let be the moment of inertia.

[0139] S5. Determine the braking torque based on the additional yaw moment.

[0140] The braking torque includes the front wheel braking torque. With rear wheel braking torque :

[0141] ; ;

[0142] ;

[0143] In the formula, The braking forces are for the front and rear wheels, respectively. The vertical loads are for the front and rear wheels, respectively. These are the track widths of the front and rear wheels, respectively. For the wheel radius, To add yaw moment.

[0144] like Figure 2 As shown, Figure 2 The figure shows the simulation results of the vehicle trajectory and the actual target trajectory of the vehicle using the proposed solution for vehicle stability control. As can be seen from the figure, the trajectory of the vehicle using the proposed solution basically coincides with the target trajectory. The proposed solution focuses on the impact of lateral error on the high-speed stability of the vehicle, and can more clearly analyze the control parameters that affect the vehicle stability, thereby further meeting the requirements of high-speed driving of special vehicles.

[0145] The vehicle stability control method based on model predictive four-wheel steering provided in Embodiment 1 of this invention incorporates the four-wheel steering system into the model predictive control framework, while employing a sliding mode control strategy for the additional yaw moment, fully leveraging the advantages of both control methods. This fusion control method effectively compensates for the shortcomings of traditional methods that rely solely on model predictive control, significantly improving vehicle stability and active safety.

[0146] Example 2

[0147] like Figure 3 As shown, in Embodiment 2 of the present invention, a vehicle stability control system based on model prediction four-wheel steering is provided, the system comprising:

[0148] Equation module 1 is used to construct a two-degree-of-freedom model of the vehicle's four-wheel steering, and to perform a state-space transformation on the two-degree-of-freedom model of the four-wheel steering to obtain the state-space equations;

[0149] Variable module 2 is used to construct the vehicle's trajectory tracking error model and determine the state variables of the model predictive controller based on the trajectory tracking error model;

[0150] Discrete module 3 is used to discretize the state space equation to obtain a discrete model, and input the discrete model and the state variables of the model predictive controller into the model predictive controller for prediction optimization to obtain the output control quantity;

[0151] Additional module 4 is used to determine the additional yaw moment required for the vehicle in sliding mode control calculation based on the output control quantity;

[0152] Torque module 5 is used to determine the braking torque based on the additional yaw torque;

[0153] The discrete module 3 includes:

[0154] The first variable submodule is used to input the discrete model and the state variables of the model predictor controller into the model predictor controller to construct new state variables. :

[0155] ;

[0156] In the formula, To be in discrete time steps Control input;

[0157] The second variable submodule is used to base the new state variable. Determine at discrete time step New state variables :

[0158] ;

[0159] ;

[0160] In the formula, These are the expanded system matrix and the expanded second input matrix, respectively.

[0161] The prediction submodule is used to predict the new state variables for subsequent discrete time steps to obtain the prediction equation:

[0162] ;

[0163] In the formula, These are the control time domain and the prediction time domain, respectively. This is the noise matrix;

[0164] The optimization submodule is used to determine the objective optimization function. :

[0165] ;

[0166] ; ;

[0167] ;

[0168] In the formula, , These are the state weighting matrix, control weighting matrix, and incremental control matrix, respectively. These are the reference values ​​for the predicted yaw rate and the expected yaw rate, respectively. These are the reference values ​​for the predicted and expected centroid sideslip angles, respectively. In discrete time steps Rear wheel steering angle, center of gravity sideslip angle, To be in discrete time steps The increment of the rear wheel steering angle, These are the maximum rear wheel steering angle, the maximum change in rear wheel steering angle, and the maximum center of gravity sideslip angle, respectively.

[0169] The solution submodule is used to combine the objective optimization function and the prediction equation to solve for the output control quantity, wherein the output control quantity is the rear wheel steering angle. ;

[0170] ;

[0171] In the formula, These are the total lateral stiffness of the front wheels and the total lateral stiffness of the rear wheels, respectively. The sideslip angle is the angle of the center of mass. For the longitudinal speed of the vehicle, These are the distances from the center of gravity to the front and rear axles, respectively. The yaw rate is angular velocity. For the front wheel steering angle, For vehicle weight, These are the parameters of the first sliding surface. To determine the desired centroid sideslip angle, The coefficient of the first discontinuous term. The sliding surface is the side slip angle of the centroid.

[0172] In other embodiments of the present invention, the present invention provides the following technical solution: a computer, including a memory 102, a processor 101, and a computer program stored in the memory 102 and executable on the processor 101, wherein the processor 101 executes the computer program to implement the vehicle stability control method based on model prediction four-wheel steering as described above.

[0173] Specifically, the processor 101 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of the present invention.

[0174] The memory 102 may include a large-capacity memory for data or instructions. For example, and not limitingly, the memory 102 may include a hard disk drive (HDD), a floppy disk drive, a solid-state drive (SSD), flash memory, an optical disk drive, a magneto-optical disk drive, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 102 may include removable or non-removable (or fixed) media. Where appropriate, the memory 102 may be internal or external to a data processing device. In a particular embodiment, the memory 102 is non-volatile memory. In a particular embodiment, the memory 102 includes read-only memory (ROM) and random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable read-only memory (PROM), an erasable read-only memory (EPROM), an electrically erasable read-only memory (EEPROM), an electrically alterable read-only memory (EAROM), or flash memory, or a combination of two or more of these. Where appropriate, the RAM can be Static Random-Access Memory (SRAM) or Dynamic Random-Access Memory (DRAM). DRAM can be Fast Page Mode Dynamic Random Access Memory (FPMDRAM), Extended Data Out Dynamic Random Access Memory (EDODRAM), Synchronous Dynamic Random-Access Memory (SDRAM), etc.

[0175] The memory 102 can be used to store or cache various data files that need to be processed and / or used for communication, as well as possible computer program instructions executed by the processor 101.

[0176] The processor 101 reads and executes the computer program instructions stored in the memory 102 to implement the above-mentioned model-predictive four-wheel steering vehicle stability control method.

[0177] In some embodiments, the computer may further include a communication interface 103 and a bus 100. For example, Figure 4 As shown, the processor 101, memory 102, and communication interface 103 are connected through bus 100 and complete communication with each other.

[0178] The communication interface 103 is used to enable communication between the various modules, devices, units, and / or equipment in the embodiments of the present invention. The communication interface 103 can also enable data communication with other components such as external devices, image / data acquisition devices, databases, external storage, and image / data processing workstations.

[0179] Bus 100 includes hardware, software, or both, that couples components of a computer device together. Bus 100 includes, but is not limited to, at least one of the following: data bus, address bus, control bus, expansion bus, and local bus. For example, and not as a limitation, bus 100 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 100 may include one or more buses. Although specific buses are described and illustrated in the embodiments of the present invention, the present invention is contemplated by any suitable bus or interconnect.

[0180] The computer can acquire a model-predictive four-wheel steering-based vehicle stability control system and execute the model-predictive four-wheel steering-based vehicle stability control method of the present invention, thereby realizing model-predictive four-wheel steering-based vehicle stability control.

[0181] In some further embodiments of the present invention, in conjunction with the above-described model-predictive four-wheel steering-based vehicle stability control method, the present invention provides the following technical solution: a storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the above-described model-predictive four-wheel steering-based vehicle stability control method.

[0182] Those skilled in the art will understand that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0183] More specific examples of readable media (a non-exhaustive list) include: electrical connections (electronic devices) with one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0184] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0185] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0186] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A vehicle stability control method based on model-predicted four-wheel steering, characterized in that, include: A two-degree-of-freedom model of the vehicle's four-wheel steering is constructed, and the state space transformation is performed on the four-wheel steering two-degree-of-freedom model to obtain the state space equation; Construct a trajectory tracking error model for the vehicle, and determine the state variables of the model predictive controller based on the trajectory tracking error model; The state-space equations are discretized to obtain a discrete model. The discrete model and the state variables of the model predictive controller are input into the model predictive controller for prediction optimization to obtain the output control quantity. Based on the output control quantity, the additional yaw moment required by the vehicle is calculated using sliding mode control. The braking torque is determined based on the additional yaw moment.

2. The vehicle stability control method based on model prediction four-wheel steering according to claim 1, characterized in that, The four-wheel steering two-degree-of-freedom model is as follows: ； In the formula, These are the total lateral stiffness of the front wheels and the total lateral stiffness of the rear wheels, respectively. The sideslip angle is the angle of the centroid. For the longitudinal speed of the vehicle, These are the distances from the center of gravity to the front and rear axles, respectively. The yaw rate is angular velocity. These are the front wheel steering angle and the rear wheel steering angle, respectively. For vehicle weight, The first derivative of the centroid sideslip angle. For rotational inertia, It is the first derivative of the yaw rate.

3. The vehicle stability control method based on model prediction four-wheel steering according to claim 2, characterized in that, The state-space equation is: ； ； ； In the formula, State vector The first derivative, These are the system matrix and the input matrix, respectively. To control the input vector.

4. The vehicle stability control method based on model prediction four-wheel steering according to claim 2, characterized in that, The trajectory tracking error model is as follows: ； ； ； ； ； ； In the formula, Vehicle lateral speed The first derivative, , They are respectively the yaw angles The first derivative and the second derivative, , These are the x-coordinate distances of the vehicle in the global coordinate system. Distance on the vertical axis The first derivative.

5. The vehicle stability control method based on model-predicted four-wheel steering according to claim 1, characterized in that, The model predicts the controller state variables, including lateral velocity deviation. Heading angle deviation lateral speed of vehicles and yaw rate : ； ； ； ； In the formula, These are the yaw angle and the reference yaw angle, respectively. For the longitudinal speed of the vehicle, The sideslip angle is the angle of the centroid. For the lateral angle The first derivative.

6. The vehicle stability control method based on model-predicted four-wheel steering according to claim 1, characterized in that, The discrete model is as follows: ； ； ； ； ； In the formula, In discrete time steps , The state vector, , , These are the discrete state matrix, discrete input matrix, and output matrix, respectively. , In discrete time steps The system outputs and control inputs, These are the system matrix and the input matrix, respectively. For discrete sampling time, Discrete time steps The sideslip angle and yaw rate of the center of mass. In discrete time steps , The state increment, To be in discrete time steps The control input increment, To be in discrete time steps The control input, It is an identity matrix.

7. The vehicle stability control method based on model prediction four-wheel steering according to claim 6, characterized in that, The step of inputting the discrete model and the state variables of the model predictive controller into the model predictive controller for prediction optimization to obtain the output control quantity includes: The discrete model and the state variables of the model predictive controller are input into the model predictive controller to construct new state variables. : ； In the formula, To be in discrete time steps Control input; Based on the new state variables Determine at discrete time step New state variables : ； ； In the formula, These are the expanded system matrix and the expanded second input matrix, respectively. The new state variables for subsequent discrete time steps are predicted to obtain the prediction equation: ； In the formula, These are the control time domain and the prediction time domain, respectively. This is the noise matrix; Determine the objective optimization function : ； ； ； ； In the formula, , These are the state weighting matrix, control weighting matrix, and incremental control matrix, respectively. These are the reference values ​​for the predicted yaw rate and the expected yaw rate, respectively. These are the reference values ​​for the predicted and expected centroid sideslip angles, respectively. In discrete time steps Rear wheel steering angle, center of gravity sideslip angle, To be in discrete time steps The increment of the rear wheel steering angle, These are the maximum rear wheel steering angle, the maximum change in rear wheel steering angle, and the maximum center of gravity sideslip angle, respectively. The objective optimization function and the prediction equation are combined to solve for the output control quantity, wherein the output control quantity is the rear wheel steering angle. ; ； In the formula, These are the total lateral stiffness of the front wheels and the total lateral stiffness of the rear wheels, respectively. The sideslip angle is the angle of the centroid. For the longitudinal speed of the vehicle, These are the distances from the center of gravity to the front and rear axles, respectively. The yaw rate is angular velocity. For the front wheel steering angle, For vehicle weight, These are the parameters of the first sliding surface. To determine the desired centroid sideslip angle, The coefficient of the first discontinuous term. The sliding surface is the side slip angle of the centroid.

8. The vehicle stability control method based on model prediction four-wheel steering according to claim 1, characterized in that, The additional yaw moment for: ； In the formula, These are the total lateral stiffness of the front wheels and the total lateral stiffness of the rear wheels, respectively. The sideslip angle is the angle of the centroid. For the longitudinal speed of the vehicle, These are the distances from the center of gravity to the front and rear axles, respectively. The yaw rate is angular velocity. These are the front wheel steering angle and the rear wheel steering angle, respectively. For the parameters of the second sliding surface, For the desired yaw rate, The coefficient of the second discontinuous term. The sliding surface is the yaw rate. Let be the moment of inertia.

9. The vehicle stability control method based on model-predicted four-wheel steering according to claim 1, characterized in that, The braking torque includes the front wheel braking torque. With rear wheel braking torque : ； ； ； In the formula, The braking forces are for the front and rear wheels, respectively. The vertical loads are for the front and rear wheels, respectively. These are the track widths of the front and rear wheels, respectively. For the wheel radius, To add yaw moment.

10. A vehicle stability control system based on model-predictive four-wheel steering, characterized in that, The system includes: The equation module is used to construct a two-degree-of-freedom model of the vehicle's four-wheel steering, and to perform a state-space transformation on the two-degree-of-freedom model of the four-wheel steering to obtain the state-space equations. The variable module is used to construct the vehicle's trajectory tracking error model and determine the state variables of the model predictive controller based on the trajectory tracking error model. The discrete module is used to discretize the state-space equations to obtain a discrete model. The discrete model and the state variables of the model predictive controller are input into the model predictive controller for prediction optimization to obtain the output control quantity. An additional module is used to calculate the additional yaw moment required by the vehicle for sliding mode control based on the output control quantity; A torque module is used to determine the braking torque based on the additional yaw moment.

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