Method and device for controlling four-wheel steering vehicle under non-rigid road surface

Abstract

The invention provides a control method and device for a four-wheel steering vehicle on a non-rigid road surface, and relates to the technical field of vehicle control. Firstly, a four-wheel steering vehicle kinematic model is established, then according to a rollover moment balance equation caused by centrifugal force and a soil sinking model caused by load transfer, a rollover model of a vehicle on a non-rigid road surface is constructed, and the rollover model comprises a steering load transfer model, a steering vehicle rollover model and a vehicle steering quasi-static rollover model. Then designing an MPC model prediction transverse controller according to a four-wheel steering vehicle kinematics model, introducing a potential field method into feedback compensation to complete setting of a fuzzy controller, and finally, calculating the position of the vehicle according to the four-wheel steering vehicle kinematics model, a rollover model of the vehicle on a non-rigid road surface, the MPC transverse controller and the fuzzy controller. And the four-wheel steering vehicle under the non-rigid road surface is controlled. Therefore, the control accuracy of the four-wheel steering vehicle under the non-rigid road surface is improved.

Description

Technical Field

[0001] The present application relates to the field of vehicle control technology, and in particular to a method and device for controlling a four-wheel steering vehicle on a non-rigid road surface. Background Art

[0002] Autonomous driving technology is becoming an important part of the modern transportation system, aiming to improve road safety, transportation efficiency, and reduce energy consumption. Among many technologies, path tracking control strategy is the basis for ensuring that vehicles travel safely along the predetermined route. Autonomous driving technology is also applied to informal road environments, such as unmanned soil turning vehicles, unmanned seeding vehicles, and unmanned soil sampling vehicles in farmlands. In complex environments such as farmlands, path tracking technology faces the challenge of large curvature steering due to natural factors such as ground conditions and road deformation. In addition, the high center of gravity problem caused by the high ground clearance design also needs special consideration. In this context, improving the tracking and stability of vehicles under large curvature conditions has become a necessary requirement.

[0003] In the prior art, when performing low-speed, large-curvature tracking steering, there is an understeering problem due to the use of front-wheel tracking and rear-wheel stability control. Regarding the stability control of high-center-of-gravity vehicles, although the prior art has improved the high-speed steering stability of ordinary high-center-of-gravity vehicles, it rarely considers large-curvature steering in farmland environments. In addition, the prior art mostly uses lateral load transfer rate or roll angle as stability evaluation criteria, but on non-rigid roads, vehicle stability is greatly affected by the environment. Therefore, combining environmental factors and considering lateral force balance as a rollover evaluation criterion can optimize the stability results. For nonlinear models, the accuracy of predictive control is limited.

[0004] In summary, how to improve the accuracy of four-wheel steering vehicle control on non-rigid roads is a problem that technical personnel in this field need to solve urgently. Summary of the invention

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

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

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

[0008] According to 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 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] Designing an MPC model predictive lateral controller based on the kinematic model of the four-wheel steering vehicle;

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

[0011] The four-wheel steering vehicle on the non-rigid road surface is controlled according to the kinematic model of the four-wheel steering vehicle, the rollover model of the vehicle on the 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] The vehicle kinematic equation is determined according to the four-wheel steering vehicle kinematic model and the physical characteristics of the vehicle when turning; the vehicle kinematic equation is used to determine the centripetal acceleration of the vehicle.

[0014] Optionally, the potential field method is introduced into the feedback compensation to complete the setting of the fuzzy controller, including:

[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 kinematic model of the four-wheel steering vehicle is a bicycle model with front and rear wheel steering, and the front and rear steering angles of the kinematic model of the four-wheel steering vehicle both satisfy the Ackerman steering model.

[0017] In a second aspect, the present application provides a four-wheel steering vehicle control device on a non-rigid road surface, comprising:

[0018] Establish a module for establishing a kinematic model of a four-wheel steering vehicle;

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

[0020] A design module, for designing an MPC model predictive lateral controller according to the kinematic model of the four-wheel steering vehicle;

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

[0022] A control module is used to control the four-wheel steering vehicle on a non-rigid road surface according to 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 comprises:

[0024] The determination module is used to determine the vehicle kinematic equation according to the four-wheel steering vehicle kinematic model and the physical characteristics of the vehicle when turning; the vehicle kinematic equation is used to determine the centripetal acceleration of the vehicle.

[0025] Optionally, the introduction module includes:

[0026] The 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 to complete the setting of the fuzzy controller.

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

[0028] In a third aspect, an embodiment of the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, a four-wheel steering vehicle control method on a non-rigid road surface as described in any one of the implementation methods in the first aspect of the embodiment of the present application is implemented.

[0029] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which instructions are stored. When the instructions are executed on a terminal device, the terminal device executes a four-wheel steering vehicle control method on a non-rigid road surface as described in any one of the implementation methods in the first aspect of the embodiment of the present application.

[0030] The present application provides a control method for a four-wheel steering vehicle on a non-rigid road surface. When executing the method, a kinematic model of a four-wheel steering vehicle is first established, and then a rollover model of the vehicle on a non-rigid road surface is constructed according to the rollover moment balance equation caused by centrifugal force and the soil subsidence model caused by load transfer, wherein the rollover model includes a steering load transfer model, a steering vehicle rollover model, and a vehicle steering quasi-static rollover model, and then according to the kinematic model of the four-wheel steering vehicle, an MPC model prediction lateral controller is designed, and then a potential field method is introduced into feedback compensation to complete the setting of a fuzzy controller, and finally, according to 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, the four-wheel steering vehicle on a non-rigid road surface is controlled. In this way, through the MPC model prediction, the path tracking accuracy of the four-wheel steering in the large curvature steering scene is significantly improved, and combined with the improved fuzzy potential field method, the stability of the high-center-of-gravity vehicle in the non-rigid road environment is enhanced, the influence of soil subsidence on the vehicle stability is considered, the control effect is optimized, and the accuracy of the control of the four-wheel steering vehicle on the non-rigid road surface is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in this embodiment or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0032] Figure 1 A flowchart of a four-wheel steering vehicle control method on a non-rigid road surface is provided for an embodiment of the present application;

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

[0034] Figure 3 A schematic diagram of the Ackerman steering model provided in an embodiment of the present application;

[0035] Figure 4 A schematic diagram of a vehicle rollover moment balance model provided in an embodiment of the present application;

[0036] Figure 5 A schematic diagram of a vehicle speed inclination angle calculation model provided in an embodiment of the present application;

[0037] Figure 6 A schematic diagram of fuzzy control provided in an embodiment of the present application;

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

[0039] Figure 8 A schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. The present application provides a four-wheel steering vehicle control method and device under non-rigid road conditions, which are used in the field of vehicle control technology. The above is only an example and does not limit the application field of the method and device provided by the present application.

[0041] Autonomous driving technology is becoming an important part of the modern transportation system, aiming to improve road safety, transportation efficiency, and reduce energy consumption. Among many technologies, path tracking control strategy is the basis for ensuring that vehicles travel safely along the predetermined route. Autonomous driving technology is also applied to informal road environments, such as unmanned soil turning vehicles, unmanned seeding vehicles, and unmanned soil sampling vehicles in farmlands. In complex environments such as farmlands, path tracking technology faces the challenge of large curvature steering due to natural factors such as ground conditions and road deformation. In addition, the high center of gravity problem caused by the high ground clearance design also needs special consideration. In this context, improving the tracking and stability of vehicles under large curvature conditions has become a necessary requirement.

[0042] In the prior art, when performing low-speed, large-curvature tracking steering, there is an understeering problem due to the use of front-wheel tracking and rear-wheel stability control. Regarding the stability control of high-center-of-gravity vehicles, although the prior art has improved the high-speed steering stability of ordinary high-center-of-gravity vehicles, it rarely considers large-curvature steering in farmland environments. In addition, the prior art mostly uses lateral load transfer rate or roll angle as stability evaluation criteria, but on non-rigid roads, vehicle stability is greatly affected by the environment. Therefore, combining environmental factors and considering lateral force balance as a rollover evaluation criterion can optimize the stability results. For nonlinear models, the accuracy of predictive control is limited.

[0043] After research, the inventor proposed the technical solution of this application. First, a kinematic model of a four-wheel steering vehicle is established. Then, according to 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 a non-rigid road surface is constructed. The rollover model includes a steering load transfer model, a steering vehicle roll model, and a vehicle steering quasi-static rollover model. Then, according to the kinematic model of the four-wheel steering vehicle, the MPC model predicts the lateral controller to design, and then the potential field method is introduced into the feedback compensation to complete the setting of the fuzzy controller. Finally, according to 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, the four-wheel steering vehicle on a non-rigid road surface is controlled. In this way, through the MPC model prediction, the path tracking accuracy of the four-wheel steering in the large curvature steering scene is significantly improved. Combined with the improved fuzzy potential field method, the stability of the high-center-of-gravity vehicle in the non-rigid road environment is enhanced, the influence of soil subsidence on the vehicle stability is considered, the control effect is optimized, and the accuracy of the control of the four-wheel steering vehicle on the non-rigid road surface is improved.

[0044] In order to enable those skilled in the art to better understand the present application, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work 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 relevant invention are shown in the accompanying drawings. In the absence of conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0045] See also Figure 1 , Figure 1 A flowchart of a four-wheel steering vehicle control method on a non-rigid road surface is provided for an embodiment of the present application, including:

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

[0047] The speed range of the soil sampling robot during steering is medium and low speed. In order to reduce the complexity of the model and the amount of calculation, the influence of tire deformation during steering is ignored, and a vehicle kinematic model that is more in line with the actual situation is used as the vehicle steering model. The front and rear steering angles both satisfy the Ackerman steering model, and the vehicle model is simplified into a bicycle model with front and rear wheels steering. Figure 2 As shown, Figure 2 A schematic diagram of a bicycle model provided in an embodiment of the present application, Figure 3 A schematic diagram of the Ackerman steering model provided in an embodiment of the present application.

[0048] The four wheels steer based on the principle of a steering center point, and the front and rear wheels are in a center point relationship with the front and rear virtual center wheel turning angles.

[0049] According to the physical characteristics of the vehicle when turning, the vehicle kinematic equation is as follows:

[0050]

[0051] Where v is the speed of the vehicle, is the heading angle of the vehicle, δ f ,δ r are the front wheel turning angle and rear wheel turning angle of the vehicle, L is the length of the vehicle body, and the centripetal acceleration a of the vehicle 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, a rollover model of the vehicle on a non-rigid road surface is constructed.

[0054] When a vehicle makes a large curvature turn, the centrifugal force generated by inertia will push the vehicle outward, causing a balance in the rollover moment. If the lateral acceleration of the vehicle is too large, it will cause an unbalanced force, which will cause a rollover. In a muddy environment, the tires are prone to sinking because the soil is soft and the vehicle exerts a large pressure on the ground. When turning, due to load transfer, the inside and outside of the vehicle sink differently, causing the body to have a roll angle. The generation of this roll angle will reduce the vehicle's roll threshold. Therefore, in order to comprehensively consider the stability of the vehicle when making a large curvature turn in mud, it is necessary to combine the rollover moment balance equation caused by centrifugal force and the soil sinking model caused by load transfer to construct a rollover model of the vehicle on a non-rigid road surface. The rollover model includes a steering load transfer model, a steering vehicle roll model, and a vehicle steering quasi-static rollover model.

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

[0056] Since the experimental vehicle is a four-wheel drive, the general tire vertical load calculation model is not applicable, and the vehicle body is assumed to be a rigid body, and the suspension stiffness and damping are not considered. First, the static load of the vehicle is calculated, and then the lateral load is distributed according to the dynamic characteristics of the vehicle when turning, and the inner and outer tire loads are calculated. It is assumed that the front and rear mass of the vehicle are evenly distributed, so the weight distribution percentage of the front and rear axles is 50%. In static conditions, the load distribution of each tire is even, and the load calculation formula for a single tire is as follows:

[0057]

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

[0059] The acceleration of the vehicle in the horizontal plane when turning will produce an equivalent force at its center of mass. This force will cause a torque that attempts to rotate the vehicle around its center of mass, resulting in a redistribution of the tire loads. The lateral force turning torque is:

[0060] M r =a c mh;

[0061] where a c is the lateral acceleration of the vehicle, and h is the height of the vehicle's center of gravity.

[0062] Since the vehicle mass is uniform and the front and rear wheelbases are approximately equal, the load change of each tire can be expressed as ΔW, which is calculated as follows:

[0063]

[0064] where t w The wheelbase of the vehicle.

[0065] Since the vehicle is a four-wheel independent drive, there is no difference between the driving wheels and the driven wheels during driving, so the load distribution between the front and rear is also approximately even. When turning, the load on the outer tire is W l , the internal tire load is W r , the calculation formula is as follows:

[0066]

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

[0068] The contact between the wheel and the soil is a complex force analysis process, because the pressure caused by the wheel pressing down cannot be simply considered. At the same time, the shear force formed by the rolling of the wheel and the ability of the soil to deform need to be considered. Therefore, the Bekker settlement theory is introduced as a calculation model for wheel settlement. The Bekker settlement formula is derived through theoretical and experimental work and can estimate the depth of the wheel's settlement on the soil surface. It takes into account the physical properties of the soil, such as the cohesion, friction angle, and compression modulus of the soil. Therefore, the formula for the wheel settlement z is as follows:

[0069]

[0070] Where W is the wheel load, k′ c , are the dimensionless soil cohesion and friction deformation moduli, d is the wheel diameter, n is the soil settlement deformation index, s is the slip rate, s = (ωr-v) / v, ω is the wheel angular velocity, r represents the effective rolling radius of the tire, v is the travel speed of the whole machine; b is the wheel width.

[0071] According to the changes in wheel load, the analysis shows that the outer wheel sinks more when turning, while the inner tire sinks less, which creates a height difference at the chassis, resulting in an additional roll angle for the entire vehicle body.

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

[0073]

[0074] Here, Δz is the difference between the inner and outer tire sinkage.

[0075]

[0076] The quasi-static rollover model of vehicle steering is as follows:

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

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

[0079]

[0080] Where β is the road roll angle, F zl is the vertical moment of the outer tire, and the following equation is obtained after simplification:

[0081]

[0082] when a c When increasing F zl Decrease, F zl = 0, the vehicle begins to roll over, and the acceleration of the vehicle rollover threshold is

[0083]

[0084] Since in this scenario, combined with the roll angle conclusion obtained by the above-mentioned steering tire-soil model, β is a negative value, the rollover threshold will be correspondingly reduced, and stability analysis is required.

[0085] The vehicle roll stability threshold is derived based on the vehicle roll stability moment balance equation. According to the tire-steering model on non-rigid roads, the threshold will change according to the lateral acceleration of the steering, which will cause the vehicle to be more prone to roll on non-rigid roads, thus requiring analysis and stability control.

[0086] A feedback loop is introduced to compensate for the vehicle's speed and rear wheel angle when the vehicle's lateral acceleration is too high, reducing them by a certain amount to ensure the vehicle's lateral stability.

[0087] Although speed compensation alone can also ensure that the lateral acceleration of the vehicle is reduced, the speed change rate is too large when controlled alone, which will cause more additional pitching moments in the longitudinal direction of the vehicle, which may cause the wheel to sink more, and the rapid change of vehicle speed will also affect the following performance of the wheel. Therefore, reducing the rear wheel steering for compensation will make the wheel speed change smoother. At the same time, a small change in the rear wheel steering has less impact on the vehicle's following performance than the front wheel. Therefore, the dual compensation control of speed and rear wheel steering can ensure the wheel roll stability while having a smaller impact on the vehicle's path following performance.

[0088] Since the above roll stability model is a complex nonlinear model, a control algorithm that can handle the system with inaccurate model or uncertainty is needed. Fuzzy control can handle the uncertainty of input data through set fuzzy rules, and can achieve multi-input and multi-output control. Fuzzy control is used for feedback control here, which can smoothly control the decision-making process within the fuzzy set and effectively improve the lateral stability of the vehicle while weighing the vehicle tracking performance.

[0089] S103: Designing an MPC model predictive lateral controller according to a kinematic model of a four-wheel steering vehicle.

[0090] Since traditional two-wheel steering has steering angle constraints, there is a loss of tracking accuracy when steering at a large rate. However, when four wheels are involved in steering at the same time, the vehicle can travel along the predetermined path more accurately. In large curvature steering scenarios, the MPC algorithm can significantly improve the vehicle's handling and safety with its precise path tracking, dynamic response optimization, constraint processing and multi-objective optimization capabilities. Therefore, based on the needs, combined with the existing four-wheel steering dynamics model, a four-wheel steering MPC controller is designed as a path tracking module.

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

[0092] First, establish a state equation system for the vehicle kinematics equation. The system can be regarded as a state quantity: The input is u(v,δ f ,δ r ) control system. The reference trajectory is the input vehicle reference path, and its general form is in u r (v r ,δ f ,δ r ) T After discretization, the vehicle discrete equation is obtained:

[0093]

[0094] in

[0095]

[0096] Where T is the sampling time. In order to constrain the control quantity increment, a new state quantity is constructed:

[0097]

[0098] The new state space equation is obtained:

[0099] ξ(k+1)=Aξ(k)+BΔu(k);

[0100] in,

[0101]

[0102] Among them, m is the prediction step length and n is the control step length. The objective function design adopts the following form:

[0103]

[0104] Where Q and R are weight matrices, both of which are assumed to be unit matrices. This formula reflects the system's ability to follow the reference trajectory and the constraints on the control quantity. The control quantity limit constraint expression 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 quantity 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 control the vehicle when its lateral acceleration exceeds the rollover threshold, which may lead to rollover due to insufficient control. Artificially lowering the rollover threshold will significantly reduce the tracking performance. Therefore, this technology introduces the potential field method into the feedback compensation. By applying a repulsive potential field, the lateral acceleration of the vehicle is controlled in advance, so that it can be smoothly adjusted before approaching the rollover threshold, thereby improving safety performance.

[0110] If the difference between the vehicle's lateral acceleration and the rollover threshold is simply used as the fuzzy control input, it is difficult to set reasonable fuzzy rules for effective control due to the large fluctuation and high rate of change of the difference. In order to ensure the absolute safety of the vehicle, it is necessary to ensure that the vehicle's lateral acceleration will never reach the rollover threshold. Therefore, referring to the idea of ​​the potential field method, the Coulomb force model between electrons is introduced as the repulsive potential field between the rollover threshold and the real-time lateral acceleration of the vehicle.

[0111] The characteristic of the repulsive potential field is that outside a certain distance range, the repulsive potential field can be ignored and does not affect the vehicle's following performance; when approaching a certain distance, the repulsive force rises steadily, and when approaching, its potential field force approaches infinity, thus ensuring that the fuzzy controller can adjust the stability within the most suitable range to avoid rollover. The Coulomb force model equation is as follows:

[0112]

[0113] where F c is the size of the repulsive potential field, k is the setting coefficient, is the rollover threshold, and ac is the real-time lateral acceleration of the vehicle.

[0114] The input domain is designed based on the improved potential field distribution of the potential field method. Six subsets are used, and the 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, while controlling the width of the maximum output set to prevent over-compensation.

[0115] Five subsets are set for the error, and the fuzzy rule adjusts the size of the compensation value according to the comparison relationship between the error and the repulsive force, so that it gradually increases. Through the fuzzy set defined above, the centroid method is used for fuzzy reasoning, and the formula is as follows:

[0116]

[0117] Among them, x i is the value of the output variable, u i is x i The corresponding membership or activation degree.

[0118] S105: Controlling the four-wheel steering vehicle on the non-rigid road surface according to the kinematic model of the four-wheel steering vehicle, the rollover model of the vehicle on the non-rigid road surface, the MPC lateral controller and the fuzzy controller.

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

[0120] The above are some specific implementations of the four-wheel steering vehicle control method on a non-rigid road provided by the embodiment of the present application. Based on this, the present application also provides a corresponding device. The device provided by the embodiment of the present application will be introduced from the perspective of functional modularization.

[0121] See also Figure 7 , Figure 7 The schematic diagram of the structure of a four-wheel steering vehicle control device for a non-rigid road surface provided in an embodiment of the present application, the four-wheel steering vehicle control device for a non-rigid road surface 700 comprises:

[0122] Establishing module 710, for establishing a kinematic model of a four-wheel steering vehicle;

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

[0124] A design module 730, for designing an MPC model predictive lateral controller according to the four-wheel steering vehicle kinematic model;

[0125] An introduction module 740 is used to introduce the potential field method into the feedback compensation to complete the setting of the fuzzy controller;

[0126] The control module 750 is used to control the four-wheel steering vehicle on a non-rigid road surface according to 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 equation according to the four-wheel steering vehicle kinematic model and the physical characteristics of the vehicle when turning; the vehicle kinematic equation is used to determine the centripetal acceleration of the vehicle.

[0129] Optionally, the introduction module 740 includes:

[0130] The 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 to complete the setting of the fuzzy controller.

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

[0132] The embodiments of the present application also provide corresponding devices and computer storage media for implementing the solutions provided by the embodiments of the present application.

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

[0134] Bus 04 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor or a local bus using any of a variety of bus structures. For example, these architectures include but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus and Peripheral Component Interconnect (PCI) bus.

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

[0136] The 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. The computer device 01 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 11 may be used to read and write non-removable, non-volatile magnetic media ( Figure 8not shown, usually called a "hard drive"). Although Figure 8 Not shown in the figure, a disk drive for reading and writing a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 04 via one or more data medium interfaces. The memory 08 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present invention.

[0137] A program / utility 12 having a set (at least one) of program modules 13 may be stored, for example, in the memory 08, such program modules 13 including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination thereof may include an implementation of a network environment. The program modules 13 generally perform the functions and / or methods of the embodiments described herein.

[0138] The computer device 01 may also communicate with one or more external devices 02 (e.g., keyboard, pointing device, display 07, etc.), one or more devices that enable a user to interact with the computer device 01, and / or any device that enables the computer device 01 to communicate with one or more other computing devices (e.g., network card, modem, etc.). Such communication may be performed via input / output (I / O) interface 06. Furthermore, the computer device 01 may also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN) and / or public network, such as the Internet) via network adapter 05. Figure 8 As shown, the network adapter 05 communicates with other modules of the computer device 01 via the bus 04. It should be understood that although Figure 8 Not shown, other hardware and / or software modules may be used in conjunction with computer device 01, 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 the programs stored in the system memory 08, for example, implementing a four-wheel steering vehicle control method on a non-rigid road surface provided in an embodiment of the present application.

[0140] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0141] Through the description of the above implementation methods, it can be known that those skilled in the art can clearly understand that all or part of the steps in the above-mentioned embodiment method can be implemented by means of software plus a general hardware platform. Based on such an understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a storage medium, such as a read-only memory (ROM) / RAM, a magnetic disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network communication device such as a router) to execute the methods described in each embodiment of the present application or some parts of the embodiments.

[0142] Each embodiment in this specification is described in a progressive manner, and the same and similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative work.

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

Claims

1. A four-wheel steering vehicle control method on a non-rigid road surface, characterized in that: include: Establish a kinematic model of a four-wheel steering vehicle; According to 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 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; Designing an MPC model predictive lateral controller based on the kinematic model of the four-wheel steering vehicle; The potential field method is introduced into feedback compensation to complete the setting of fuzzy controller; The four-wheel steering vehicle on the non-rigid road surface is controlled according to the kinematic model of the four-wheel steering vehicle, the rollover model of the vehicle on the 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 comprises: The vehicle kinematic equation is determined according to the four-wheel steering vehicle kinematic model and the physical characteristics of the vehicle when turning; the vehicle kinematic equation is used to determine the centripetal acceleration of the vehicle.

3. The method according to claim 1, characterized in that The potential field method is introduced into the feedback compensation to complete the setting of the fuzzy controller, including: 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.

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

5. A four-wheel steering vehicle control device on a non-rigid road surface, characterized in that: include: Establish a module for establishing a kinematic model of a four-wheel steering vehicle; A construction module is used to construct a rollover model of a vehicle on a non-rigid road surface according to a rollover moment balance equation caused by centrifugal force and a soil subsidence model caused by load transfer; the rollover model includes a steering load transfer model, a steering vehicle roll model, and a vehicle steering quasi-static rollover model; A design module, for designing an MPC model predictive lateral controller according to the kinematic model of the four-wheel steering vehicle; An introduction module is used to introduce the potential field method into the feedback compensation to complete the setting of the fuzzy controller; A control module is used to control the four-wheel steering vehicle on a non-rigid road surface according to 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.

6. The device according to claim 5, characterized in that The device also includes: The determination module is used to determine the vehicle kinematic equation according to the four-wheel steering vehicle kinematic model and the physical characteristics of the vehicle when turning; the vehicle kinematic equation is used to determine the centripetal acceleration of the vehicle.

7. The device according to claim 5, characterized in that The introduction module comprises: The 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 to complete the setting of the fuzzy controller.

8. The device according to claim 5, characterized in that The kinematic model of the four-wheel steering vehicle is a bicycle model with front and rear wheels steering, and the front and rear steering angles of the kinematic model of the four-wheel steering vehicle both satisfy the Ackerman steering model.

9. 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 when the processor executes the computer program, a four-wheel steering vehicle control method under a non-rigid road surface as described in any one of claims 1 to 4 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed on a terminal device, the terminal device executes the four-wheel steering vehicle control method on a non-rigid road surface as described in any one of claims 1-4.

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