A control method and device for a four-wheel steering vehicle under a non-rigid road surface

Abstract

This application provides a control method and device for four-wheel steering vehicles on non-rigid road surfaces, relating to the field of vehicle control technology. First, a kinematic model of the four-wheel steering vehicle is established. Then, based on the rollover moment balance equation caused by centrifugal force and the soil subsidence model caused by load transfer, a rollover model of the vehicle on non-rigid road surfaces is constructed. The rollover model includes a steering load transfer model, a steering vehicle roll model, and a quasi-static rollover model. Next, based on the four-wheel steering vehicle kinematic model, a predictive lateral controller (MPC) is designed. Then, the potential field method is introduced into the feedback compensation to complete the setting of the fuzzy controller. Finally, based on the four-wheel steering vehicle kinematic model, the rollover model on non-rigid road surfaces, the MPC lateral controller, and the fuzzy controller, the four-wheel steering vehicle on non-rigid road surfaces is controlled. This improves the accuracy of four-wheel steering vehicle control on non-rigid road surfaces.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and in particular to a method and device for controlling four-wheel steering vehicles on non-rigid road surfaces. Background Technology

[0002] Autonomous driving technology is becoming an important component of modern transportation systems, aiming to improve road safety, transportation efficiency, and reduce energy consumption. Among the many technologies, path tracking control strategies are fundamental to ensuring vehicles travel safely along predetermined routes. Autonomous driving technology is also being applied to informal road environments, such as unmanned tillage vehicles, unmanned seeding vehicles, and unmanned soil sampling vehicles in farmland. In complex environments like farmland, path tracking technology faces challenges in high-curvature steering due to natural factors such as ground conditions and road surface deformation. Furthermore, the high center of gravity resulting from high ground clearance design also requires special consideration. Against this backdrop, improving vehicle tracking performance and stability under high curvature conditions has become a necessary requirement.

[0003] In existing technologies, understeer is a problem during low-speed, high-curvature tracking steering due to the use of front-wheel tracking and rear-wheel stabilization control. While existing technologies have improved high-speed steering stability for vehicles with high centers of gravity, they rarely consider high-curvature steering in agricultural environments. Furthermore, existing technologies often use lateral load transfer rate or roll angle as stability evaluation criteria, but vehicle stability is significantly affected by the environment on non-rigid surfaces. Therefore, incorporating environmental factors and considering lateral force balance as a rollover evaluation criterion can optimize stability results. For nonlinear models, the accuracy of predictive control is limited.

[0004] In conclusion, improving the accuracy of four-wheel steering vehicle control on non-rigid road surfaces is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] In view of this, this application provides a method and apparatus for controlling four-wheel steering vehicles on non-rigid road surfaces, aiming to improve the accuracy of four-wheel steering vehicle control on non-rigid road surfaces.

[0006] In a first aspect, this application provides a method for controlling a four-wheel steering vehicle on a non-rigid road surface, including:

[0007] Establish a kinematic model for a four-wheel steering vehicle;

[0008] Based on the rollover moment balance equation caused by centrifugal force and the soil subsidence model caused by load transfer, a rollover model of a vehicle on a non-rigid road surface is constructed; the rollover model includes a steering load transfer model, a steering vehicle rollover model, and a vehicle steering quasi-static rollover model.

[0009] Based on the kinematic model of the four-wheel steering vehicle, a predictive lateral controller for the MPC model is designed.

[0010] The potential field method is introduced into the feedback compensation to complete the setting of the fuzzy controller;

[0011] The four-wheel steering vehicle is controlled based on the kinematic model of the four-wheel steering vehicle, the rollover model of the vehicle on a non-rigid road surface, the MPC lateral controller, and the fuzzy controller.

[0012] Optionally, after establishing the kinematic model of the four-wheel steering vehicle, the method further includes:

[0013] Based on the four-wheel steering vehicle kinematic model and the physical characteristics of the vehicle during steering, the vehicle kinematic equations are determined; the vehicle kinematic equations are used to determine the vehicle's centripetal acceleration.

[0014] Optionally, the incorporation of the potential field method into the feedback compensation to complete the setting of the fuzzy controller includes:

[0015] A Coulomb force model between electrons is introduced as a repulsive potential field between the roll threshold and the real-time lateral acceleration of the vehicle to complete the setting of the fuzzy controller.

[0016] Optionally, the four-wheel steering vehicle kinematic model is a bicycle model with steering on both the front and rear wheels, and the front and rear steering angles of the four-wheel steering vehicle kinematic model both satisfy the Ackermann steering model.

[0017] Secondly, this application provides a four-wheel steering vehicle control device for non-rigid road surfaces, comprising:

[0018] A module is created to build the kinematic model of a four-wheel steering vehicle;

[0019] The module is used to construct a rollover model of a vehicle on a non-rigid road surface based on the rollover moment balance equation caused by centrifugal force and the soil subsidence model caused by load transfer; the rollover model includes a steering load transfer model, a steering vehicle rollover model and a vehicle steering quasi-static rollover model.

[0020] The design module is used to design the MPC model predictive lateral controller based on the kinematic model of the four-wheel steering vehicle.

[0021] An introduction module is used to incorporate the potential field method into the feedback compensation to complete the setup of the fuzzy controller;

[0022] The control module is used to control the four-wheel steering vehicle on a non-rigid road surface based on the kinematic model of the four-wheel steering vehicle, the rollover model of the vehicle on a non-rigid road surface, the MPC lateral controller, and the fuzzy controller.

[0023] Optionally, the device further includes:

[0024] The determination module is used to determine the vehicle kinematic equations based on the four-wheel steering vehicle kinematic model and the physical characteristics of the vehicle during steering; the vehicle kinematic equations are used to determine the centripetal acceleration of the vehicle.

[0025] Optionally, the introducing module includes:

[0026] An introduction unit is used to introduce a Coulomb force model between electrons as a repulsive potential field between the roll threshold and the real-time lateral acceleration of the vehicle, in order to complete the setting of the fuzzy controller.

[0027] Optionally, the four-wheel steering vehicle kinematic model is a bicycle model with steering on both the front and rear wheels, and the front and rear steering angles of the four-wheel steering vehicle kinematic model both satisfy the Ackermann steering model.

[0028] Thirdly, embodiments of this application provide a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the four-wheel steering vehicle control method under non-rigid road surfaces as described in any embodiment of the first aspect of this application.

[0029] Fourthly, embodiments of this application provide a computer-readable storage medium storing instructions that, when executed on a terminal device, cause the terminal device to perform a four-wheel steering vehicle control method on a non-rigid road surface as described in any of the embodiments of the first aspect of this application.

[0030] This application provides a control method for four-wheel steering vehicles on non-rigid road surfaces. In implementing the method, a kinematic model of the four-wheel steering vehicle is first established. Then, based on the rollover moment balance equation caused by centrifugal force and the soil subsidence model caused by load transfer, a rollover model of the vehicle on non-rigid road surfaces is constructed. This rollover model includes a steering load transfer model, a steering vehicle roll model, and a quasi-static rollover model. Next, based on the four-wheel steering vehicle kinematic model, an MPC model predictive lateral controller is designed. Then, the potential field method is introduced into the feedback compensation to complete the setting of the fuzzy controller. Finally, based on the four-wheel steering vehicle kinematic model, the rollover model on non-rigid road surfaces, the MPC lateral controller, and the fuzzy controller, the four-wheel steering vehicle on non-rigid road surfaces is controlled. Thus, through MPC model prediction, the path tracking accuracy of four-wheel steering in high-curvature steering scenarios is significantly improved. Combined with the improved fuzzy potential field method, the stability of vehicles with high centers of gravity in non-rigid road surface environments is enhanced. The impact of soil subsidence on vehicle stability is considered, optimizing the control effect and thus improving the accuracy of four-wheel steering vehicle control on non-rigid road surfaces. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in this embodiment or the prior art, the drawings used in the description of the embodiment or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 A flowchart of a four-wheel steering vehicle control method under non-rigid road surfaces is provided in this application embodiment;

[0033] Figure 2 A schematic diagram of a bicycle model provided in an embodiment of this application;

[0034] Figure 3 A schematic diagram of the Ackermann steering model provided in the embodiments of this application;

[0035] Figure 4 A schematic diagram of the vehicle rollover moment balance model provided in the embodiments of this application;

[0036] Figure 5 A schematic diagram of the vehicle speed tilt angle calculation model provided in the embodiments of this application;

[0037] Figure 6 This is a schematic diagram of fuzzy control provided for an embodiment of this application;

[0038] Figure 7A schematic diagram of the structure of a four-wheel steering vehicle control device under non-rigid road surface provided in this application embodiment;

[0039] Figure 8 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0040] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. This application provides a four-wheel steering vehicle control method and apparatus for non-rigid road surfaces, relating to the field of vehicle control technology. The above are merely examples and do not limit the application field of the method and apparatus provided in this application.

[0041] Autonomous driving technology is becoming an important component of modern transportation systems, aiming to improve road safety, transportation efficiency, and reduce energy consumption. Among the many technologies, path tracking control strategies are fundamental to ensuring vehicles travel safely along predetermined routes. Autonomous driving technology is also being applied to informal road environments, such as unmanned tillage vehicles, unmanned seeding vehicles, and unmanned soil sampling vehicles in farmland. In complex environments like farmland, path tracking technology faces challenges in high-curvature steering due to natural factors such as ground conditions and road surface deformation. Furthermore, the high center of gravity resulting from high ground clearance design also requires special consideration. Against this backdrop, improving vehicle tracking performance and stability under high curvature conditions has become a necessary requirement.

[0042] In existing technologies, understeer is a problem during low-speed, high-curvature tracking steering due to the use of front-wheel tracking and rear-wheel stabilization control. While existing technologies have improved high-speed steering stability for vehicles with high centers of gravity, they rarely consider high-curvature steering in agricultural environments. Furthermore, existing technologies often use lateral load transfer rate or roll angle as stability evaluation criteria, but vehicle stability is significantly affected by the environment on non-rigid surfaces. Therefore, incorporating environmental factors and considering lateral force balance as a rollover evaluation criterion can optimize stability results. For nonlinear models, the accuracy of predictive control is limited.

[0043] The inventors, through research, proposed the technical solution of this application. First, a kinematic model of a four-wheel steering vehicle is established. Then, based on the rollover moment balance equation caused by centrifugal force and the soil subsidence model caused by load transfer, a rollover model of the vehicle on non-rigid road surfaces is constructed. This rollover model includes a steering load transfer model, a steering vehicle roll model, and a quasi-static rollover model. Next, based on the four-wheel steering vehicle kinematic model, an MPC model predictive lateral controller is designed. Then, the potential field method is introduced into the feedback compensation to complete the setting of the fuzzy controller. Finally, based on the four-wheel steering vehicle kinematic model, the rollover model on non-rigid road surfaces, the MPC lateral controller, and the fuzzy controller, the four-wheel steering vehicle on non-rigid road surfaces is controlled. In this way, through MPC model prediction, the path tracking accuracy of four-wheel steering in high-curvature steering scenarios is significantly improved. Combined with the improved fuzzy potential field method, the stability of vehicles with high centers of gravity in non-rigid road surface environments is enhanced. The impact of soil subsidence on vehicle stability is considered, the control effect is optimized, and thus the accuracy of four-wheel steering vehicle control on non-rigid road surfaces is improved.

[0044] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are only a part of the embodiments of the present application, and not all of them. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present application. It should be noted that, for ease of description, only the parts related to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of the present application can be combined with each other.

[0045] See Figure 1 , Figure 1 A flowchart of a four-wheel steering vehicle control method under non-rigid road surfaces is provided in this application embodiment, including:

[0046] S101: Establish a kinematic model for a four-wheel steering vehicle.

[0047] The soil sampling robot operates at low to medium speeds during turning. To reduce model complexity and computational load, the impact of tire deformation during turning is ignored, and a more realistic vehicle kinematics model is used as the vehicle steering model. This ensures that both front and rear steering angles satisfy the Ackermann steering model, simplifying the vehicle model to a bicycle model with front and rear wheel steering. Figure 2 As shown, Figure 2 A schematic diagram of a bicycle model provided in an embodiment of this application. Figure 3 This is a schematic diagram of the Ackermann steering model provided in an embodiment of this application.

[0048] The four wheels are turned using a single steering center point, with the front and rear wheels having a single center point relationship with the front and rear virtual center wheels.

[0049] Based on the physical characteristics of the vehicle during steering, the vehicle's kinematic equations are as follows:

[0050]

[0051] Where v is the vehicle speed. Let δ be the heading angle of the vehicle. f δ r These are the front wheel steering angle and rear wheel steering angle, respectively; L is the vehicle length; and a is the vehicle's centripetal acceleration. c It is derived from the following formula:

[0052]

[0053] S102: Based on the rollover moment balance equation caused by centrifugal force and the soil subsidence model caused by load transfer, construct a rollover model of a vehicle on a non-rigid road surface.

[0054] When a vehicle makes a sharp turn, the centrifugal force generated by inertia pushes the vehicle outward, thus balancing the rollover moment. If the vehicle's lateral acceleration is too large, it will lead to an imbalance of forces, causing a rollover. In muddy environments, due to the soft ground and the high pressure exerted by the vehicle on the ground, the tires are prone to sinking. During steering, due to load transfer, the amount of sinking on the inner and outer sides of the vehicle differs, resulting in a roll angle. This roll angle lowers the vehicle's roll threshold. Therefore, to comprehensively consider the stability of a vehicle making sharp turns in mud, it is necessary to combine the rollover moment balance equation caused by centrifugal force and the mud sinking model caused by load transfer to construct a rollover model of the vehicle on non-rigid surfaces. This rollover model includes a steering load transfer model, a steering vehicle roll model, and a quasi-static rollover model of the vehicle during steering.

[0055] The specific model for the steering load transfer is as follows;

[0056] Since the experimental vehicle is four-wheel drive, the general tire vertical load calculation model is not applicable, and the vehicle body is assumed to be rigid, neglecting suspension stiffness and damping. First, static load calculations are performed, then lateral load distribution is calculated based on the vehicle's dynamic characteristics during steering, calculating the loads on the inner and outer tires. It is assumed that the vehicle's front-to-rear mass distribution is uniform, therefore the weight distribution percentage between the front and rear axles is 50%. Under static conditions, the load distribution on each tire is uniform, and the formula for calculating the load on a single tire is as follows:

[0057]

[0058] Where m is the mass of the vehicle and g is the acceleration due to gravity.

[0059] When a vehicle turns, the acceleration on the horizontal plane generates an equivalent force at its center of mass. This force results in a torque that attempts to rotate the vehicle about its center of mass, leading to a redistribution of tire load. The lateral force overturning torque is:

[0060] M r =a c mh;

[0061] Where a c Let θ be the lateral acceleration of the vehicle, and h be the height of the vehicle's center of gravity.

[0062] Since the vehicle has a uniform mass and the front and rear wheelbases are approximately equal, the load variation of each tire can be expressed as ΔW. The calculation method for ΔW is as follows:

[0063]

[0064] Where t w This refers to the vehicle's wheelbase.

[0065] Because the vehicle has four-wheel independent drive, there is no distinction between drive wheels and driven wheels during driving, resulting in a nearly uniform load distribution between the front and rear. The load on the outer tire during steering is W. l The inner tire load is W. r The calculation formula is as follows:

[0066]

[0067] The specific model of vehicle roll during steering is as follows:

[0068] The contact between a wheel and soil is a complex force analysis process. It's not sufficient to simply consider the pressure caused by the wheel's downward movement; the shear force generated by the wheel's rolling and the soil's deformation capacity must also be taken into account. Therefore, the Bekker subsidence theory is introduced as a calculation model for wheel subsidence. The Bekker subsidence formula, derived through theoretical and experimental work, can estimate the depth of subsidence caused by a wheel on the soil surface. It considers the physical properties of the soil, such as soil cohesion, friction angle, and compression modulus. The formula for the wheel subsidence amount z is shown below:

[0069]

[0070] Where W is the wheel load, and k′ c , Here, denoted as dimensionless soil cohesion and frictional deformation modulus, respectively; d is the wheel diameter; n is the soil subsidence deformation index; s is the slip ratio; s = (ωr - v) / v; ω is the wheel angular velocity; r represents the effective rolling radius of the tire; v is the overall machine travel speed; and b is the wheel width.

[0071] Analysis of wheel load changes revealed that the outer wheel sinks more during steering, while the inner tire sinks less, creating a height difference at the chassis and resulting in an additional roll angle for the vehicle body.

[0072] like Figure 4 , Figure 5 As shown, Figure 4 This is a schematic diagram of the vehicle rollover moment balance model provided in an embodiment of this application. Figure 5 This is a schematic diagram of the vehicle speed tilt angle calculation model provided in the embodiments of this application. The tilt angle caused by the difference in soil subsidence height is β, which can be calculated by the following formula.

[0073]

[0074] Where Δz is the difference between the inner and outer tire depressions.

[0075]

[0076] The specific static rollover model for vehicle steering is as follows:

[0077] Before conducting a study on vehicle roll performance, it is necessary to make assumptions about the vehicle based on actual conditions. Since the soil sampling robot has a four-wheel drive structure and its suspension system is relatively stiff, the vehicle is assumed to be a rigid body without suspension. Its lateral torque is generated by the combined action of centrifugal force and gravity when the vehicle turns. Based on physical principles and force balance reasoning, a rollover model of the rollover vehicle is derived.

[0078] Since β is generally small, assuming sinβ≈β and cosβ≈1, the equation for the vehicle's lateral equilibrium moment is as follows:

[0079]

[0080] Where β is the road slope angle, F zl Let the vertical moment of the outer tire be denoted as , which, after simplification, yields the following equation:

[0081]

[0082] when a c When F increases zl Decrease, F zl When the acceleration is 0, the vehicle begins to roll over. Therefore, the acceleration threshold for vehicle rollover is:

[0083]

[0084] In this scenario, based on the roll angle conclusions derived from the aforementioned steering tire-soil model, β is a negative value, thus the rollover threshold will be significantly reduced, necessitating stability analysis.

[0085] The vehicle roll stability threshold was derived based on the vehicle roll stability moment balance equation. According to the tire-steering model, the threshold changes with the lateral acceleration of the steering on non-rigid surfaces. This makes the vehicle more prone to roll on non-rigid surfaces, thus requiring analysis and stability control.

[0086] A feedback loop is introduced to compensate for excessive lateral acceleration of the vehicle by reducing the vehicle's speed and rear wheel angle.

[0087] While speed compensation alone can reduce lateral acceleration, the excessive rate of speed change during independent control can lead to additional pitching moments in the vehicle's longitudinal direction, potentially increasing wheel sinking. Rapid speed changes can also affect wheel tracking performance. Therefore, simultaneously reducing rear-wheel steering as compensation results in smoother wheel speed changes. Small changes in rear-wheel steering have less impact on vehicle tracking performance than front-wheel steering. Thus, dual compensation control of speed and rear-wheel steering can ensure wheel roll stability while minimizing the impact on vehicle path tracking performance.

[0088] Since the aforementioned roll stability model is a complex nonlinear model, a control algorithm capable of handling systems with inaccurate or uncertain models is required. Fuzzy control can handle the uncertainty of input data through defined fuzzy rules and can achieve multi-input, multi-output control. Here, fuzzy control is used for feedback control, which can smooth the control decision-making process within the fuzzy set, effectively improving the vehicle's lateral stability while balancing vehicle tracking performance.

[0089] S103: Design a predictive lateral controller for the MPC model based on the kinematic model of a four-wheel steering vehicle.

[0090] Traditional two-wheel steering suffers from steering angle constraints, leading to a loss of tracking accuracy during high-curvature steering. However, with all four wheels involved in steering simultaneously, the vehicle can more accurately follow the predetermined path. In high-curvature steering scenarios, the MPC algorithm, with its precise path tracking, dynamic response optimization, constraint handling, and multi-objective optimization capabilities, can significantly improve vehicle handling and safety. Therefore, based on the required specifications and existing four-wheel steering dynamics models, a four-wheel steering MPC controller is designed as the path tracking module.

[0091] The MPC controller solves an optimization problem that satisfies the objective function and various constraints to obtain the optimal control sequence in the prediction time domain. In order to use the model in the design of the MPC controller, the model needs to be discretized.

[0092] First, establish a system of state equations relating to the vehicle's kinematics. The system can be viewed as a single state variable. The input is u(v,δ) f ,δ r The control system of ). The reference trajectory is the input vehicle reference path, which generally takes the form of in u r (v r ,δ f ,δ r ) T After discretization, the vehicle's discrete equations are obtained:

[0093]

[0094] in

[0095]

[0096] Where T is the sampling time. To constrain the control increment, a new state variable is constructed:

[0097]

[0098] The new state-space equations are obtained:

[0099] ξ(k+1)=Aξ(k)+BΔu(k);

[0100] in,

[0101]

[0102] Where m is the prediction step size and n is the control step size. The objective function is designed in the following form:

[0103]

[0104] Where Q and R are weight matrices, both set to identity matrices. This equation reflects the system's ability to follow the reference trajectory and the constraints on the control input, respectively. The expression for the control input limit constraint is as follows:

[0105] u min (T+k)≤u(T+k)≤u max (T+k), k=0,1,...,Nc-1;

[0106] The constraints on the control increment are as follows:

[0107] Δu min (T+k)≤Δu(T+k)≤Δu max (T+k), k=0,1,...,Nc-1.

[0108] S104: Introduce the potential field method into the feedback compensation to complete the setting of the fuzzy controller.

[0109] It is obviously dangerous to take control when the vehicle's lateral acceleration exceeds the rollover threshold, as this could lead to insufficient control and a rollover. Manually lowering the rollover threshold, on the other hand, would significantly reduce tracking performance. Therefore, this technology introduces a potential field method into the feedback compensation. By applying a repulsive potential field, the vehicle's lateral acceleration is pre-controlled, allowing for a smooth adjustment before approaching the rollover threshold, thereby improving safety performance.

[0110] Simply using the difference between the vehicle's lateral acceleration and the rollover threshold as the fuzzy control input is insufficient due to the large fluctuations and high rate of change in this difference, making it difficult to set reasonable fuzzy rules for effective control. To ensure absolute vehicle safety, it is necessary to ensure that the vehicle's lateral acceleration never reaches the rollover threshold. Therefore, referencing the potential field method, a Coulomb force model between electrons is introduced as the repulsive potential field between the rollover threshold and the vehicle's real-time lateral acceleration.

[0111] The characteristic of the repulsive potential field is that outside a certain distance, the repulsive potential field is negligible and does not affect the vehicle's following performance; as it approaches a certain distance, the repulsive force increases steadily, and its potential force approaches infinity just before reaching the target distance. This ensures that the fuzzy controller can make stability adjustments within the most suitable range, preventing rollover. The Coulomb force model equation is as follows:

[0112]

[0113] Where F c The magnitude of the repulsive potential field is given by k, which is a set coefficient. α represents the rollover threshold, and ac represents the vehicle's real-time lateral acceleration.

[0114] The input universe of discourse is designed based on the improved potential field distribution of the potential field method. Six subsets are used, and a generalized bell-shaped membership function (GBELLMF) is adopted to ensure that corresponding compensation can be provided at each stage during the gradual increase of force. At the same time, the width of the maximum output set is controlled to prevent overcompensation.

[0115] Five subsets are defined for the error. The fuzzy rules adjust the compensation value based on the comparison between the error and the repulsive force, gradually increasing the value. Using the fuzzy sets defined above, fuzzy inference is performed using the centroid method, with the following formula:

[0116]

[0117] Where, x i It outputs the value of the variable, u i It is x i The corresponding membership degree or activation level.

[0118] S105: Based on the kinematic model of a four-wheel steering vehicle, the rollover model of the vehicle on a non-rigid road surface, the MPC lateral controller, and the fuzzy controller, the four-wheel steering vehicle on a non-rigid road surface is controlled.

[0119] like Figure 6 As shown, Figure 6 This is a fuzzy control diagram provided in an embodiment of the present application. The reference path is input, and fuzzy control is performed based on the kinematic model of a four-wheel steering vehicle, the rollover model of the vehicle on a non-rigid road surface, the MPC lateral controller, and the fuzzy controller.

[0120] The above are some specific implementations of the four-wheel steering vehicle control method under non-rigid road surfaces provided in this application. Based on this, this application also provides a corresponding device. The device provided in this application will be described below from the perspective of functional modularity.

[0121] See Figure 7 , Figure 7 This application provides a schematic diagram of the structure of a four-wheel steering vehicle control device for non-rigid road surfaces. The four-wheel steering vehicle control device 700 for non-rigid road surfaces includes:

[0122] Module 710 is established to create a kinematic model of a four-wheel steering vehicle.

[0123] The construction module 720 is used to construct a rollover model of a vehicle on a non-rigid road surface based on the rollover moment balance equation caused by centrifugal force and the soil subsidence model caused by load transfer; the rollover model includes a steering load transfer model, a steering vehicle rollover model and a vehicle steering quasi-static rollover model.

[0124] Design module 730 is used to design the MPC model predictive lateral controller based on the kinematic model of the four-wheel steering vehicle.

[0125] Module 740 is introduced to incorporate the potential field method into the feedback compensation in order to complete the setup of the fuzzy controller.

[0126] The control module 750 is used to control the four-wheel steering vehicle on a non-rigid road surface based on the kinematic model of the four-wheel steering vehicle, the rollover model of the vehicle on a non-rigid road surface, the MPC lateral controller, and the fuzzy controller.

[0127] Optionally, the device 700 further includes:

[0128] The determination module is used to determine the vehicle kinematic equations based on the four-wheel steering vehicle kinematic model and the physical characteristics of the vehicle during steering; the vehicle kinematic equations are used to determine the centripetal acceleration of the vehicle.

[0129] Optionally, the introducing module 740 includes:

[0130] An introduction unit is used to introduce a Coulomb force model between electrons as a repulsive potential field between the roll threshold and the real-time lateral acceleration of the vehicle, in order to complete the setting of the fuzzy controller.

[0131] Optionally, the four-wheel steering vehicle kinematic model is a bicycle model with steering on both the front and rear wheels, and the front and rear steering angles of the four-wheel steering vehicle kinematic model both satisfy the Ackermann steering model.

[0132] This application also provides corresponding devices and computer storage media for implementing the solutions provided in this application.

[0133] like Figure 8 As shown, the computer device 01 is represented in the form of a general-purpose computing device. The components of the computer device 01 may include, but are not limited to: one or more processors or processing units 03, system memory 08, and bus 04 connecting different system components (including system memory 08 and processing unit 03).

[0134] Bus 04 represents one or more of several bus architectures, including memory buses or memory controllers, peripheral buses, graphics acceleration ports, processors, or local buses using any of the various bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0135] Computer device 01 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by computer device 01, including volatile and non-volatile media, removable and non-removable media.

[0136] System memory 08 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 09 and / or cache memory 10. Computer device 01 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 11 may be used to read and write non-removable, non-volatile magnetic media (…). Figure 8Not shown; usually referred to as a "hard drive"). Although Figure 8 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 04 via one or more data media interfaces. Memory 08 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0137] A program / utility 12 having a set (at least one) of program modules 13 may be stored in, for example, memory 08. Such program modules 13 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 13 typically perform the functions and / or methods described in the embodiments of the present invention.

[0138] Computer device 01 can also communicate with one or more external devices 02 (e.g., keyboard, pointing device, display 07, etc.), and with one or more devices that enable a user to interact with the computer device 01, and / or with any device that enables the computer device 01 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed through input / output (I / O) interface 06. Furthermore, computer device 01 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) through network adapter 05. Figure 8 As shown, network adapter 05 communicates with other modules of computer device 01 via bus 04. It should be understood that, although... Figure 8 As not shown in the diagram, it can be used in conjunction with computer device 01 with other hardware and / or software modules, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0139] The processor unit 03 executes various functional applications and data processing by running programs stored in the system memory 08, such as implementing a four-wheel steering vehicle control method under non-rigid road surfaces provided in the embodiments of this application.

[0140] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0141] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that all or part of the steps in the methods of the above embodiments can be implemented by means of software plus a general-purpose hardware platform. Based on this understanding, the technical solution of this application can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as a read-only memory (ROM) / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, a server, or a network communication device such as a router) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0142] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on its differences from other embodiments. In particular, the apparatus embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0143] The above description is merely an exemplary implementation of this application and is not intended to limit the scope of protection of this application.

Claims

1. A method for controlling a four-wheel steering vehicle on a non-rigid road surface, characterized in that, include: Establish a kinematic model for a four-wheel steering vehicle; Based on the rollover moment balance equation caused by centrifugal force and the soil subsidence model caused by load transfer, a rollover model of a vehicle on a non-rigid road surface is constructed. Based on the kinematic model of the four-wheel steering vehicle, a predictive lateral controller for the MPC model is designed. A Coulomb force model between electrons is introduced as a repulsive potential field between the roll threshold and the real-time lateral acceleration of the vehicle to complete the setting of the fuzzy controller; the four-wheel steering vehicle under non-rigid road surface is controlled according to the kinematic model of the four-wheel steering vehicle, the rollover model of the vehicle under non-rigid road surface, the MPC lateral controller and the fuzzy controller.

2. The method according to claim 1, characterized in that, After establishing the kinematic model of the four-wheel steering vehicle, the method further includes: Based on the four-wheel steering vehicle kinematic model and the physical characteristics of the vehicle during steering, the vehicle kinematic equations are determined; the vehicle kinematic equations are used to determine the vehicle's centripetal acceleration.

3. The method according to claim 1, characterized in that, The kinematic model of the four-wheel steering vehicle is a bicycle model with steering on both the front and rear wheels, and the front and rear steering angles of the kinematic model of the four-wheel steering vehicle both satisfy the Ackermann steering model.

4. A four-wheel steering vehicle control device for non-rigid road surfaces, characterized in that, include: A module is created to build the kinematic model of a four-wheel steering vehicle; The module is used to construct a rollover model of a vehicle on a non-rigid road surface based on the rollover moment balance equation caused by centrifugal force and the soil subsidence model caused by load transfer. The design module is used to design the MPC model predictive lateral controller based on the kinematic model of the four-wheel steering vehicle. An introduction module is used to incorporate the potential field method into the feedback compensation to complete the setup of the fuzzy controller; The introducing module includes: An introduction unit is used to introduce the Coulomb force model between electrons as the repulsive potential field between the roll threshold and the real-time lateral acceleration of the vehicle, so as to complete the setting of the fuzzy controller. The control module is used to control the four-wheel steering vehicle on a non-rigid road surface based on the kinematic model of the four-wheel steering vehicle, the rollover model of the vehicle on a non-rigid road surface, the MPC lateral controller, and the fuzzy controller.

5. The apparatus according to claim 4, characterized in that, The device further includes: The determination module is used to determine the vehicle kinematic equations based on the four-wheel steering vehicle kinematic model and the physical characteristics of the vehicle during steering; the vehicle kinematic equations are used to determine the centripetal acceleration of the vehicle.

6. The apparatus according to claim 4, characterized in that, The kinematic model of the four-wheel steering vehicle is a bicycle model with steering on both the front and rear wheels, and the front and rear steering angles of the kinematic model of the four-wheel steering vehicle both satisfy the Ackermann steering model.

7. A computer device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the four-wheel steering vehicle control method under non-rigid road surfaces as described in any one of claims 1-3.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed on a terminal device, cause the terminal device to perform the four-wheel steering vehicle control method under non-rigid road surfaces as described in any one of claims 1-3.

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