CN111428382A - Method, system, computer device and readable storage medium for vehicle trajectory control - Google Patents
Method, system, computer device and readable storage medium for vehicle trajectory control Download PDFInfo
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Abstract
The application relates to a method, a system, a computer device and a readable storage medium for vehicle trajectory control, wherein the method for vehicle trajectory control comprises the following steps: establishing a dynamic space model according to the transverse error, the course error and the system dynamic parameters of the vehicle by acquiring the transverse error and the course error of the vehicle, and establishing a system state space equation according to the dynamic space model; obtaining a disturbance matrix through a fuzzy control system according to the transverse error and the vehicle speed of the vehicle; according to the method and the device, a disturbance matrix is introduced into a system state space equation to obtain a corrected system state space equation, and an expected front wheel deflection angle of the vehicle is calculated according to the corrected system state space equation through an optimal control method.
Description
Technical Field
The present application relates to the field of automated driving technology, and in particular, to a method, system, computer device, and readable storage medium for vehicle trajectory control.
Background
With the development of the automobile industry technology, automobile intellectualization has attracted much attention, and as an important component of automobile intellectualization, the unmanned technology gradually becomes a standing competitive point for large manufacturers at home and abroad. The vehicle track control is an essential key technical link in the unmanned system, and the vehicle track control not only accepts an upstream positioning, sensing and decision planning module, but also is in butt joint with a downstream vehicle execution mechanism, so the quality of the track tracking performance directly relates to the driving safety and the driving experience of the unmanned vehicle. At present, the mainstream trajectory control in the engineering field mostly adopts methods such as pre-aiming control, classical feedback control, optimal control based on a state space theory and the like. However, the vehicle actually traveling is a complex nonlinear and strongly coupled system, and is widely involved, and various factors derived from uncertainty of internal parameters of the vehicle, external disturbance and the like exist.
In the related art, the controller design based on the state space theory can obtain relatively accurate control precision on the premise of reasonable matching parameters, but when the vehicle trajectory control system is disturbed in the face of extreme working conditions, the stability of the vehicle trajectory control system is reduced, the system is very easy to be not converged, and the safety of unmanned driving is influenced.
At present, an effective solution is not provided aiming at the problem that the stability of a vehicle track control system is reduced when extreme condition disturbance is faced by the design of a controller only based on a state space theory in the related technology.
Disclosure of Invention
The embodiment of the application provides a method, a system, computer equipment and a readable storage medium for controlling a vehicle track, so as to at least solve the problem that the stability of a vehicle track control system is reduced when extreme condition disturbance is faced due to the design of a controller only based on a state space theory in the related art.
In a first aspect, an embodiment of the present application provides a method for controlling a vehicle trajectory, where the method includes:
acquiring a transverse error and a course error of the vehicle, establishing a dynamic space model according to the transverse error, the course error and a system dynamic parameter, and establishing a system state space equation according to the dynamic space model;
obtaining a disturbance matrix through a fuzzy control system according to the transverse error and the vehicle speed of the vehicle;
and introducing the disturbance matrix into the system state space equation to obtain a corrected system state space equation, and calculating the expected front wheel deflection angle of the vehicle according to the corrected system state space equation by an optimal control method.
In some embodiments, the obtaining, by the fuzzy control system, the disturbance matrix includes:
acquiring a basic domain of discourse of the lateral error and a basic domain of discourse of the vehicle speed, and generating a fuzzy subset of the basic domain of discourse;
performing fuzzy reasoning according to the fuzzy subset and a fuzzy rule, and performing fuzzy solution on the fuzzy reasoning result to obtain a value of a fuzzy compensation object, wherein the fuzzy compensation object is determined according to the transverse error and the course error;
and generating the disturbance matrix according to the fuzzy compensation object.
In some of these embodiments, said calculating a desired front wheel slip angle of said vehicle from said modified system state space equation comprises:
and determining a performance function according to the state space equation of the correction system and a space controller, and calculating a front wheel deflection angle corresponding to the minimum value of the performance function to obtain the expected front wheel deflection angle.
In some embodiments, the obtaining the desired front wheel slip angle by calculating a front wheel slip angle corresponding to a minimum value of the performance function includes:
constructing a Hamiltonian of the performance function, introducing a Lagrange multiplier, and iteratively solving the Hamiltonian to obtain a state feedback matrix;
and obtaining the expected front wheel deflection angle according to the state feedback matrix and the state vector of the vehicle.
In some embodiments, the introducing the perturbation matrix into the system state space equation to obtain a modified system state space equation includes:
the corrected system state space equation comprises a system state matrix and a control matrix, the disturbance matrix comprises a system state disturbance matrix and a control disturbance matrix, the system state disturbance matrix is superposed on the system state matrix, and the control disturbance matrix is superposed on the control matrix.
In some embodiments, the lateral error and the heading error are obtained from a reference trajectory and positioning information of the vehicle.
In a second aspect, an embodiment of the present application provides a system for vehicle trajectory control, the system including: the system comprises a dynamics calculation module, a fuzzy feedback module and a control module;
the dynamic calculation module acquires the transverse error and the course error of the vehicle, establishes a dynamic space model according to the transverse error, the course error and system dynamic parameters, and establishes a system state space equation according to the dynamic space model;
the fuzzy feedback module obtains a disturbance matrix through a fuzzy control system according to the transverse error and the vehicle speed of the vehicle;
the dynamics calculation module introduces the disturbance matrix into the system state space equation to obtain a corrected system state space equation, and the control module calculates the expected front wheel deflection angle of the vehicle according to the corrected system state space equation through an optimal control method.
In some of these embodiments, the system further comprises a chassis module;
the chassis module is used for acquiring a reference track and positioning information of the vehicle and obtaining the transverse error and the course error according to the reference track and the positioning information.
In some embodiments, the fuzzy feedback module further comprises a fuzzification unit, a fuzzy inference unit, and a deblurring unit;
the fuzzification unit is used for acquiring a basic domain of discourse of the transverse error and a basic domain of discourse of the vehicle speed and generating a fuzzy subset of the basic domain of discourse;
the fuzzy reasoning unit carries out fuzzy reasoning according to the fuzzy subset and the fuzzy rule;
the ambiguity resolving unit resolves ambiguity of the fuzzy inference result to obtain a value of a fuzzy compensation object, and generates a disturbance matrix according to the fuzzy compensation object, wherein the fuzzy compensation object is determined according to the transverse error and the course error.
In a third aspect, an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements any one of the above methods when executing the computer program.
In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and the computer program is executed by a processor to implement any of the above methods.
Compared with the related art, the method for controlling the vehicle track provided by the embodiment of the application establishes a dynamic space model according to the transverse error, the course error and the system dynamic parameters by acquiring the transverse error and the course error of the vehicle, and establishes a system state space equation according to the dynamic space model; obtaining a disturbance matrix through a fuzzy control system according to the transverse error and the vehicle speed of the vehicle; the disturbance matrix is introduced into the system state space equation to obtain a corrected system state space equation, and the expected front wheel deflection angle of the vehicle is calculated according to the corrected system state space equation through an optimal control method, so that the problem that the stability of a vehicle track control system is reduced when extreme working condition disturbance is faced due to controller design only based on a state space theory in the related technology is solved, and the stability of the vehicle control system under the extreme working conditions such as large error and strong interference is improved.
The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below to provide a more thorough understanding of the application.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a schematic diagram of an application environment of a method for vehicle trajectory control according to an embodiment of the present application;
FIG. 2 is a flow chart of a method of vehicle trajectory control according to an embodiment of the present application;
FIG. 3 is a schematic view of a bicycle model in accordance with an embodiment of the present application;
FIG. 4 is a flow chart of a method of generating a perturbation matrix according to an embodiment of the present application;
FIG. 5 is a block diagram of a system for vehicle trajectory control according to an embodiment of the present application;
FIG. 6 is a block diagram of a fuzzy feedback module according to an embodiment of the present application;
FIG. 7 is a schematic diagram of a vehicle trajectory control system according to a preferred embodiment of the present application;
FIG. 8 is a diagram illustrating trace tracking simulation results according to an embodiment of the present application;
FIG. 9 is a schematic diagram of lateral error according to an embodiment of the present application;
fig. 10 is an internal structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments provided in the present application without any inventive step are within the scope of protection of the present application.
It is obvious that the drawings in the following description are only examples or embodiments of the present application, and that it is also possible for a person skilled in the art to apply the present application to other similar contexts on the basis of these drawings without inventive effort. Moreover, it should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of ordinary skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms referred to herein shall have the ordinary meaning as understood by those of ordinary skill in the art to which this application belongs. Reference to "a," "an," "the," and similar words throughout this application are not to be construed as limiting in number, and may refer to the singular or the plural. The present application is directed to the use of the terms "including," "comprising," "having," and any variations thereof, which are intended to cover non-exclusive inclusions; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to the listed steps or elements, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. "and/or" describes an association relationship of associated objects, meaning that three relationships may exist, for example, "A and/or B" may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. Reference herein to the terms "first," "second," "third," and the like, are merely to distinguish similar objects and do not denote a particular ordering for the objects.
The method for controlling the vehicle track provided by the present application can be applied to the application environment shown in fig. 1, and fig. 1 is a schematic application environment diagram of the method for controlling the vehicle track according to the embodiment of the present application, as shown in fig. 1. In the process of driving the unmanned vehicle 102, the driving track of the vehicle 102 needs to be controlled in real time, the vehicle track control system 104 installed in the vehicle 102 establishes a dynamic space model according to the lateral error, the heading error and the system dynamic parameters of the vehicle 102 by acquiring the lateral error and the heading error of the vehicle 102, and establishes a system state space equation according to the dynamic space model; the vehicle track control system 104 obtains a disturbance matrix through a fuzzy control system according to the transverse error and the speed of the vehicle 102; the disturbance matrix is introduced into the system state space equation to obtain a modified system state space equation, and the desired front wheel slip angle of the vehicle 102 is calculated according to the modified system state space equation by an optimal control method. The server 104 may be implemented as a stand-alone server or a server cluster composed of a plurality of servers.
The present embodiment provides a method for controlling a vehicle track, and fig. 2 is a flowchart of a method for controlling a vehicle track according to an embodiment of the present application, and as shown in fig. 2, the method includes the following steps:
step S201, obtaining the lateral error and the course error of the vehicle, establishing a dynamic space model according to the lateral error, the course error and the system dynamic parameters of the vehicle, and establishing a system state space equation according to the dynamic space model.
The lateral error refers to a distance error between the vehicle and a reference track in a direction perpendicular to the reference track in a frelnet (Frenet) coordinate system, and the heading error is a difference value between a target direction of the vehicle and a current direction of the vehicle, and fig. 3 is a schematic diagram of a bicycle model according to an embodiment of the application, and as shown in fig. 3, system dynamic parameters used in the embodiment are given, including a deflection angle of a vehicle tire, a stress condition of the tire and the like. When the vehicle actually moves, due to the influence of dynamic factors, the actual speed direction of the vehicle is not consistent with the aspect of wheels, which has a great influence on the actual tracking control of the vehicle track, and the transverse dynamic characteristics of the vehicle need to be considered to ensure better transverse control performance. Assuming that the vehicle is a rigid motion model, a bicycle model of the vehicle can be obtained, wherein OXY is an inertial coordinate system fixed on the ground, and oxyz is a vehicle coordinate system fixed on the vehicle body. The vehicle of the single-vehicle model has 2 degrees of freedom, including yaw motion around the z-axis and longitudinal motion along the x-axis, and the symbolic parameters in fig. 3 have the meanings shown in table 1 below, and table 1 is a symbolic table of kinetic parameters.
TABLE 1
| Name of symbol | Means of |
| Flf，Flr | Longitudinal force of front and rear tires |
| Fcf，Fcr | Front and rear tyre side force |
| Fxf，Fxr | X-direction force received by front and rear tires |
| Fyf，Fyr | Force in y direction received by front and rear tires |
| Vf | Front wheel speed |
| a | Length of front suspension |
| b | Length of rear suspension |
| δf | Front wheel declination |
| δr | Rear wheel declination |
| αf | Front wheel slip angle |
V in FIG. 3cfIs VfA velocity component in a direction perpendicular to the direction of the vehicle, VtfIs VfIn the speed component in the direction parallel to the vehicle direction, since the conventional vehicle generally does not need the rear wheel slip angle input and has a smaller front wheel slip angle, and the lateral control of the vehicle is generally realized by the front wheel slip angle input, in the bicycle model shown in fig. 3, a dynamic spatial model can be established by the force analysis of the y axis and the z axis by the newton's second law, and the dynamic spatial model takes the lateral error and the heading error as state variables, as shown in formula 1:
in equation 1, e1 is the lateral error of the vehicle with respect to the reference trajectory,
for the lateral error rate of change, e2 is the vehicle relativeIn the course of the heading error of the reference trajectory,
the course error rate of change, as input for the front wheel slip angle, matrix A is the system state matrix, determined by equation 2 below:
in the formula 2, CafFor front wheel cornering stiffness, CarFor rear wheel cornering stiffness, IzRepresenting moment of inertia, VxIs the vehicle speed.
The matrix B in equation 1 may be determined by equation 3 below, the matrix B being a control matrix:
in the formula 3, CafFor front wheel cornering stiffness, CarFor rear wheel cornering stiffness, IzRepresenting the moment of inertia.
And (3) obtaining a system state space equation according to the dynamics space model determined by the formula 1 and by combining a state space system theory.
And step S202, obtaining a disturbance matrix through a fuzzy control system according to the transverse error and the vehicle speed of the vehicle.
The fuzzy control is a computer digital control technology based on fuzzy set theory, fuzzy linguistic variables and fuzzy logic reasoning, is a nonlinear control in nature, and belongs to the field of intelligent control. The disturbance matrix is designed based on compensation of limit working conditions suffered by the unmanned vehicle in the running process and is generated by external large-error disturbance encountered by the vehicle in the running process. In the fuzzy control system, the real-time lateral error and the vehicle speed of the vehicle are input, and the elements in the disturbance matrix are output.
And S203, introducing the disturbance matrix into the system state space equation to obtain a corrected system state space equation, and calculating the expected front wheel deflection angle of the vehicle according to the corrected system state space equation by an optimal control method.
After introducing the disturbance matrix, the state variables contained in the system state space equation comprise a transverse error, a transverse error change rate, a heading error change rate and a front wheel deflection angle of the vehicle. The main problem of the optimal control method is to select an allowable control rate according to the established mathematical model of the controlled object, so that the controlled object operates according to the preset requirement, and a given certain performance index reaches a minimum value or a maximum value. In a dynamic system, an objective function of a dynamic optimal problem is a functional, and common methods for solving the dynamic optimal problem include a classical variational method, a minimum principle, a dynamic rule, a linear quadratic optimal control method and the like.
Through the steps S201 to S203, in this embodiment, based on the dynamic space model of the vehicle, the system state space equation is modified based on the strong disturbance caused by the limit condition in consideration of the road curvature characteristics of the curves with different sizes in the vehicle driving process, where the limit condition is a condition with a large lateral error or a large heading error caused by various uncertain factors in the vehicle starting or driving process. The scheme in the embodiment solves the problem that the stability of the vehicle track control system is reduced when extreme working condition disturbance is faced due to the fact that controller design based on a state space theory is adopted in the related technology.
In some embodiments, fig. 4 is a flowchart of a method of generating a perturbation matrix according to an embodiment of the present application, as shown in fig. 4, the method comprising the steps of:
step S401, acquiring the basic domain of the lateral error and the basic domain of the vehicle speed, and generating a fuzzy subset of the basic domain.
Before a fuzzy rule is formed, fuzzy segmentation needs to be carried out on an input variable space and an output variable space of a fuzzy control system to form domains, and the overlapping degree of the domains influences the performance of the fuzzy control system during the fuzzy segmentation. There is no clear method for determining the degree of domain overlap, and the segmentation method is usually determined by simulation and experimental adjustment, and the degree of overlap may be 1/3-1/2. The size of the overlap portion means the degree of blur between the blur control rules, and thus the blur segmentation is an important feature of the blur control.
In this embodiment, the basic universe of discourse for determining the input lateral error is [ -e ]max，emax]The basic discourse domain of vehicle speed is [ v ]min，vmax]And blurring the input end variables, wherein the fuzzy subset of the lateral error is { NB, NM, NS, ZE, PS, PM, PB }, and the fuzzy subset of the vehicle speed is { ZE, PS, PM, PB, PC }, wherein N represents a negative value, Z represents 0, and P represents a positive value. For example, the basic universe of lateral error may be specified as [ -1, 1 [ ]]In meters, the basic universe of discourse for vehicle speed is [0, 60 ]]In km/h, the fuzzy subset of lateral errors may be { -0.5, -0.3, -0.2, 0, 0.2, 0.3, 0.5} and the fuzzy subset of vehicle speeds may be {0, 20, 30, 50, 60 }.
And S402, performing fuzzy reasoning according to the fuzzy subset and a fuzzy rule, and resolving the fuzzy reasoning result to obtain a value of a fuzzy compensation object, wherein the incremental fuzzy compensation object is determined according to the transverse error and the course error.
The output in this embodiment is the element in the perturbation matrix, and taking the element Δ a12 as an example, the output universe is set to [ Δ a1 ]min，Δa1max]Its fuzzy subset is { NM, NS, ZE, PS, PM }. According to actual debugging experience, a triangular function is selected as a parameter membership function form, and the fuzzy inference rule in the embodiment is obtained as follows: if e and V Then delta a12, where e is the input lateral error and V is the real-time vehicle speed of the vehicle, the logic rule table of the fuzzy processing in this embodiment is shown in Table 2.
TABLE 2
After the element values of the disturbance matrix are obtained through fuzzy reasoning, the output is subjected to deblurring processing to obtain discretization output delta a12 of the fuzzy control system, and the discretization output delta a12 is used as the parameter values of the disturbance matrix to ensure the stability of the control system.
Step S403, generating the disturbance matrix according to the blur compensation object. After solving the elements in the disturbance matrix, a complete disturbance matrix can be obtained.
Through the steps S401 to S403, the disturbance matrix is generated through the fuzzy control system, the calculation method is simple, the efficiency is high, and meanwhile the robustness and the fault tolerance of the system state space equation can be effectively improved.
In some embodiments, calculating the desired front wheel slip angle for the vehicle from the modified system state space equation comprises: and determining a performance function according to the state space equation of the correction system and a space controller, and calculating the front wheel deflection angle corresponding to the minimum value of the performance function to obtain the expected front wheel deflection angle.
In this embodiment, the system control is performed based on a linear Quadratic Regulator (L initial Quadratic Regulator, abbreviated as L QR), and the form of the performance function is shown in formula 4:
in formula 4, Q is a semi-positive matrix with n × n dimensions for state weighting, R is a positive matrix with R × R dimensions for control weighting, and L QR optimal controller is designed to solve a linear feedback law of the optimal state, so that the index function J shown in formula 4 takes the minimum value.
In some embodiments, calculating the desired front wheel slip angle comprises the steps of: and constructing a Hamiltonian of the performance function, introducing a Lagrange multiplier, and iteratively solving the Hamiltonian to obtain a state feedback matrix.
The Hamilton function (Hamilton) constructed based on equation 4 in this embodiment is shown in equation 5:
in equation 5, x (t) is a vehicle state vector including a lateral error, a lateral error change rate, a heading error, and a heading error change rate, u (t) is a matrix including a front wheel slip angle, and λ is a parameter.
On the basis of formula 5, a lagrangian multiplier is introduced to construct an unconstrained optimization problem, and a state feedback matrix can be obtained by iterative solution of optimization, as shown in formula 6:
k(t)＝R-1(t)[B+ΔB]T(t) P (t) equation 6
In equation 6, k (t) is a state feedback matrix, and p (t) as an intermediate variable matrix can be obtained by solving an algebraic ricarthat (Riccati) equation as shown in equation 7:
[A+ΔA]T(t)P(t)+P(t)[A+ΔA](t)+Q(t)-P(t)[B+ΔB](t)R(t)-1[B+ΔB]T(t)P(t)＝0
equation 7 obtains the desired front wheel slip angle based on the state feedback matrix and the state vector of the vehicle. In this embodiment, according to k (t) and x (t), a formula for obtaining the desired front wheel slip angle u (t) is shown in formula 8:
u (t) ═ k (t) x (t) equation 8
Through the above formula 8, the expected front wheel slip angle of the unmanned vehicle can be obtained, and then the vehicle trajectory is controlled, and the stability of the unmanned vehicle under the limit working condition is improved.
In some embodiments, introducing a perturbation matrix into the system state space equation to obtain a modified system state space equation comprises: the corrected system state space equation comprises a system state matrix and a control matrix, wherein the disturbance matrix comprises a system state disturbance matrix and a control disturbance matrix, the system state disturbance matrix is superposed on the system state matrix, and the control disturbance matrix is superposed on the control matrix.
In this embodiment, based on a dynamic space model and in combination with a state space system theory, disturbance matrices Δ a and Δ B are introduced into a system state matrix a and a control matrix B, so as to obtain a modified system state space equation, as shown in formula 9:
in equation 9, x (t) includes the state variables of lateral error, lateral error change rate, heading error, and heading error change rate, respectively, and u (t) includes the control variable of the front wheel slip angle. A (t) is a system state matrix, B (t) is a control matrix, Delta A (t) is a time-varying uncertain matrix with the same dimension as Delta A, Delta B (t) is a time-varying uncertain matrix with the same dimension as Delta B, and Delta A and Delta B are both from external large error disturbance encountered in the driving process of the unmanned vehicle, wherein Delta A (t) and Delta B (t) are obtained by processing a transverse error and a vehicle speed through a fuzzy control system. Δ A (t) and Δ B (t) are small-range delta matrices that can take values for the non-linear relationship between detected error and real-time vehicle speed. By adjusting Δ a (t) and Δ b (t), the stability of the system can be ensured.
In some embodiments, the lateral error and the heading error are obtained from a reference trajectory and positioning information of the vehicle. In the embodiment, the transverse error and the course error are obtained based on the existing functions of the vehicle, and the calculation efficiency is improved.
It should be noted that the steps illustrated in the above-described flow diagrams or in the flow diagrams of the figures may be performed in a computer system, such as a set of computer-executable instructions, and that, although a logical order is illustrated in the flow diagrams, in some cases, the steps illustrated or described may be performed in an order different than here.
The present embodiment further provides a system for controlling a vehicle track, which is used to implement the foregoing embodiments and preferred embodiments, and the description of the system is omitted here. As used hereinafter, the terms "module," "unit," "subunit," and the like may implement a combination of software and/or hardware for a predetermined function. While the system described in the embodiments below is preferably implemented in software, implementations in hardware, or a combination of software and hardware are also possible and contemplated.
Fig. 5 is a block diagram of a system for vehicle trajectory control according to an embodiment of the present application, and as shown in fig. 5, the apparatus includes: a dynamics calculation module 51, a fuzzy feedback module 52 and a control module 53; the dynamics calculation module 51 obtains the lateral error and the course error of the vehicle, establishes a dynamics space model according to the lateral error, the course error and the system dynamics parameters of the vehicle, and establishes a system state space equation according to the dynamics space model; the fuzzy feedback module 52 obtains a disturbance matrix through a fuzzy control system according to the transverse error and the vehicle speed of the vehicle; the dynamics calculation module 51 introduces the disturbance matrix into the system state space equation to obtain a modified system state space equation through an optimal control method, and the control module 53 calculates the expected front wheel slip angle of the vehicle according to the modified system state space equation.
In the embodiment, the system state space equation is corrected through the fuzzy feedback module 52 based on the dynamics space model generated by the dynamics calculation module 51, the vehicle track is controlled through the control module 53 according to the corrected system state space equation, and the problem that the stability of the vehicle track control system is reduced when extreme condition disturbance is faced due to the fact that the controller design only based on the state space theory in the related technology is solved.
In some embodiments, the system for vehicle trajectory control further comprises a chassis module: the chassis module is used for acquiring a reference track and positioning information of the vehicle and obtaining the transverse error and the course error according to the reference track and the positioning information.
In some embodiments, fig. 6 is a block diagram of a fuzzy feedback module according to an embodiment of the present application, and as shown in fig. 6, the fuzzy feedback module 52 includes: fuzzifying unit 61, fuzzy inference unit 62, and deblurring unit 63: the fuzzification unit 61 is configured to input a basic domain of the lateral error and a basic domain of the vehicle speed, and generate a fuzzy subset of the basic domain of discourse; the fuzzy inference unit 62 performs fuzzy inference according to the fuzzy subset and the fuzzy rule; the deblurring unit 63 deblurs the fuzzy inference result to obtain a value of a fuzzy compensation object, and generates a disturbance matrix according to the fuzzy compensation object, wherein the fuzzy compensation object is determined according to the transverse error and the course error.
The embodiments of the present application are described and illustrated below by means of preferred embodiments.
Fig. 7 is a schematic diagram of a vehicle trajectory control system according to a preferred embodiment of the present application, and as shown in fig. 7, in a process of performing vehicle trajectory control, the system acquires local planning reference trajectory information issued by a vehicle planning module, and establishes a dynamic space model of a vehicle by combining positioning information given by a vehicle real-time positioning module, where the dynamic space model takes a lateral error and a heading error relative to a reference trajectory as state variables, and introduces a disturbance matrix Δ a to the system state matrix a and a disturbance matrix Δ B to the control matrix B to further implement design of an improved L QR optimal controller.
In the design process of the incremental fuzzy feedback module, the basic principle of the system is as follows: the fuzzy feedback module collects the transverse error e of the unmanned vehicle track tracking and the real-time vehicle speed V in real time as input, and outputs the elements of delta A (t) and delta B (t) through a fuzzy reasoning mechanism of the fuzzy feedback module.
Because the method mainly solves the problem of system stability under the condition of large error interference in the extreme working condition, the incremental compensation design is carried out mainly on the transverse error and the heading error, the method can determine that elements a12, a34, b11 and b31 in a system state matrix and a control matrix are objects needing incremental fuzzy compensation by referring to formula 2, fuzzy matching can be carried out according to the actual situation by considering that the value design methods of the elements in the incremental matrix delta A (t) and the delta B (t) are strictly similar, and the fuzzy control system design of the increment △ a12 is explained by taking the state matrix element a12 as an example.
In the embodiment, Simulink and Carsim vehicle simulation software are combined, relevant simulated vehicle dynamics configuration is carried out according to actual vehicle parameters, a starting large-error limit working condition scene is set for testing, a starting transverse error is set to be 3m, a vehicle speed is set to be 25KPH, a conventional L QR optimal control method is compared, fig. 8 is a schematic diagram of a track tracking simulation result according to the embodiment of the application, as shown in fig. 8, compared with a L QR scheme, a track of the scheme for improving L QR in the application is overlapped with a target track more quickly, the L QR track is fluctuated in a longer time period, fig. 9 is a schematic diagram of a transverse error according to the embodiment of the application, as shown in fig. 9, under the condition of facing the large-error scene of the limit working condition, the QR optimal control method for improving the L QR is controlled within 20cm, system oscillation is caused, the scheme of the application enables the system overshoot to be controlled within 20cm and converged quickly, unstable problems such as divergence, the dynamic performance in the tracking control process can be effectively avoided, and the overall stability of the.
The above modules may be functional modules or program modules, and may be implemented by software or hardware. For a module implemented by hardware, the modules may be located in the same processor; or the modules can be respectively positioned in different processors in any combination.
In one embodiment, a computer device is provided, which may be a terminal. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of vehicle trajectory control. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
In one embodiment, fig. 10 is a schematic diagram of an internal structure of an electronic device according to an embodiment of the present application, and as shown in fig. 10, there is provided an electronic device, which may be a server, and its internal structure diagram may be as shown in fig. 10. The electronic device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the electronic device is configured to provide computing and control capabilities. The memory of the electronic equipment comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the electronic device is used for storing data. The network interface of the electronic device is used for connecting and communicating with an external terminal through a network. The computer program is executed by a processor to implement a method of vehicle trajectory control.
Those skilled in the art will appreciate that the architecture shown in fig. 10 is merely a block diagram of some of the structures associated with the present solution and does not constitute a limitation on the electronic devices to which the present solution applies, and that a particular electronic device may include more or less components than those shown, or combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, the steps in the method for controlling the trajectory of a vehicle provided by the above embodiments are realized.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which, when being executed by a processor, implements the steps of the method for vehicle trajectory control provided by the above-mentioned various embodiments.
It will be understood by those of ordinary skill in the art that all or a portion of the processes of the methods of the embodiments described above may be implemented by a computer program that may be stored on a non-volatile computer-readable storage medium, which when executed, may include the processes of the embodiments of the methods described above, wherein any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (11)
1. A method of vehicle trajectory control, the method comprising:
acquiring a transverse error and a course error of the vehicle, establishing a dynamic space model according to the transverse error, the course error and a system dynamic parameter, and establishing a system state space equation according to the dynamic space model;
obtaining a disturbance matrix through a fuzzy control system according to the transverse error and the vehicle speed of the vehicle;
and introducing the disturbance matrix into the system state space equation to obtain a corrected system state space equation, and calculating the expected front wheel deflection angle of the vehicle according to the corrected system state space equation by an optimal control method.
2. The method of claim 1, wherein the deriving the perturbation matrix by the fuzzy control system comprises:
acquiring a basic domain of discourse of the lateral error and a basic domain of discourse of the vehicle speed, and generating a fuzzy subset of the basic domain of discourse;
performing fuzzy reasoning according to the fuzzy subset and a fuzzy rule, and performing fuzzy solution on the fuzzy reasoning result to obtain a value of a fuzzy compensation object, wherein the fuzzy compensation object is determined according to the transverse error and the course error;
and generating the disturbance matrix according to the fuzzy compensation object.
3. The method of claim 1, wherein said calculating a desired front wheel slip angle for the vehicle from the revised system state space equation comprises:
and determining a performance function according to the state space equation of the correction system and a space controller, and calculating a front wheel deflection angle corresponding to the minimum value of the performance function to obtain the expected front wheel deflection angle.
4. The method of claim 3, wherein the obtaining the desired front wheel slip angle by calculating a front wheel slip angle corresponding to a minimum value of the performance function comprises:
constructing a Hamiltonian of the performance function, introducing a Lagrange multiplier, and iteratively solving the Hamiltonian to obtain a state feedback matrix;
and obtaining the expected front wheel deflection angle according to the state feedback matrix and the state vector of the vehicle.
5. The method of claim 1, wherein the introducing the perturbation matrix into the system state space equation to obtain a modified system state space equation comprises:
the corrected system state space equation comprises a system state matrix and a control matrix, wherein the disturbance matrix comprises a system state disturbance matrix and a control disturbance matrix, the system state disturbance matrix is superposed on the system state matrix, and the control disturbance matrix is superposed on the control matrix.
6. The method of claim 1, wherein the lateral error and the heading error are derived from a reference trajectory and positioning information of the vehicle.
7. A system for vehicle trajectory control, the system comprising: the system comprises a dynamics calculation module, a fuzzy feedback module and a control module;
the dynamic calculation module acquires the transverse error and the course error of the vehicle, establishes a dynamic space model according to the transverse error, the course error and system dynamic parameters, and establishes a system state space equation according to the dynamic space model;
the fuzzy feedback module obtains a disturbance matrix through a fuzzy control system according to the transverse error and the vehicle speed of the vehicle;
the dynamics calculation module introduces the disturbance matrix into the system state space equation to obtain a corrected system state space equation, and the control module calculates the expected front wheel deflection angle of the vehicle according to the corrected system state space equation through an optimal control method.
8. The system of claim 7, further comprising a chassis module;
the chassis module is used for acquiring a reference track and positioning information of the vehicle and obtaining the transverse error and the course error according to the reference track and the positioning information.
9. The system of claim 7, wherein the fuzzy feedback module further comprises a fuzzification unit, a fuzzy inference unit, and a deblurring unit;
the fuzzification unit is used for acquiring a basic domain of discourse of the transverse error and a basic domain of discourse of the vehicle speed and generating a fuzzy subset of the basic domain of discourse;
the fuzzy reasoning unit carries out fuzzy reasoning according to the fuzzy subset and the fuzzy rule;
the ambiguity resolving unit resolves ambiguity of the fuzzy inference result to obtain a value of a fuzzy compensation object, and generates a disturbance matrix according to the fuzzy compensation object, wherein the fuzzy compensation object is determined according to the transverse error and the course error.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 6 when executing the computer program.
11. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.
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