Intelligent vehicle trajectory tracking robust control method and system

Abstract

The application discloses a kind of intelligent vehicle trajectory tracking robust control method and system, method includes the following steps: the vehicle model of target vehicle is simplified as bicycle model, establishes two degrees of freedom vehicle model;Based on two degrees of freedom vehicle model, establish vehicle trajectory tracking model;Using orthogonal decomposition method and state feedback method, establish the feedback dissipative Hamilton system of vehicle trajectory tracking model;According to the feedback dissipative Hamilton system, combined with Hamilton robust controller, design generalized Hamilton robust controller.The Hamilton robust controller designed in the application is compared with sliding mode controller and LQR controller, greatly reduces the algorithm complexity, improves the calculation efficiency, reduces the error of control vehicle tracking reference trajectory.

Description

Technical Field

[0001] This invention relates to a robust control method and system for intelligent vehicle trajectory tracking, belonging to the field of trajectory tracking technology for unmanned vehicles. Background Technology

[0002] In recent years, with the research and application of technologies such as autonomous driving, 5G networks, and vehicle-to-everything (V2X), automobiles are gradually moving beyond their role as mere transportation tools, permeating more aspects of daily life, and developing towards intelligence. Meanwhile, intelligent vehicles offer significant advantages in improving driving safety and reducing traffic accidents, thus becoming a global research hotspot. Trajectory tracking control, as one of the core issues of intelligent vehicles, is divided into lateral control and longitudinal control. Precise control of the vehicle's lateral motion can affect the safety, comfort, and economy of intelligent vehicle driving control. However, due to the highly nonlinear dynamic characteristics and parameter uncertainties of vehicle systems, the complexity of dynamic control is increased. Therefore, designing and validating reasonable trajectory tracking control strategies is of significant research importance.

[0003] Trajectory tracking control technology needs to balance both tracking accuracy and stability. Currently, commonly used control methods include PID control, sliding mode control, model predictive control, LQR control, and fuzzy control. Specifically, PID control is simple and easy to use, but its performance is poor in complex nonlinear systems and it is susceptible to disturbances; manual parameter tuning is also cumbersome. Sliding mode control is robust and can quickly respond to system changes, but the discontinuity of control may cause system chattering, and it requires full-state information of the system, which may increase system complexity. Model predictive control can model and control nonlinear systems well, but it involves a large amount of computation and requires consideration of real-time performance and stability. LQR control can directly obtain the optimal control rate by solving the Riccati equations, has a fast calculation speed, and can fully utilize system state information; however, the design of the LQR controller depends on the model equations of the controlled object, and its control effect is poor when the system model error is large. Fuzzy control can effectively control complex systems and has good tolerance to fuzziness and uncertainty, but it requires manual design of fuzzy rules and fuzzy sets, which is quite difficult. Therefore, the choice of control strategy should be based on a trade-off made according to the specific circumstances.

[0004] Currently, most solutions to the trajectory tracking problem in autonomous vehicles employ adaptive control algorithms. However, adaptive control algorithms rely on accurate models, are sensitive to initial conditions, have high computational complexity, and may result in poor real-time performance. Furthermore, the parameter convergence speed in adaptive control algorithms is slow, and inaccurate parameter estimation can lead to a degraded control system performance.

[0005] Generalized Hamiltonian control systems are a development of traditional Hamiltonian systems, describing a class of open systems with energy dissipation and energy exchange with the environment. Therefore, they represent a broader range of dynamic systems. These systems have clear structures, well-defined physical meanings, and structural integrity. The Hamiltonian function (the total energy of the system) is its quasi-Lyapunov function, thus exhibiting significant advantages in stability analysis and stabilization control. Furthermore, Hamiltonian control algorithms are simple to design and require minimal parameter tuning. Currently, Hamiltonian-based methods (also known as energy-based control methods) are widely used in the control of power systems and mechanical systems, but their application in the field of autonomous driving is limited. Summary of the Invention

[0006] To address the aforementioned issues, this invention proposes a robust control method and system for intelligent vehicle trajectory tracking, which can improve the stability and accuracy of intelligent vehicle trajectory tracking.

[0007] The technical solution adopted by this invention to solve its technical problem is as follows:

[0008] In a first aspect, an embodiment of the present invention provides a robust control method for intelligent vehicle trajectory tracking, comprising the following steps:

[0009] The vehicle model of the target vehicle is simplified into a bicycle model, and a two-degree-of-freedom vehicle model is established.

[0010] A vehicle trajectory tracking model is established based on a two-degree-of-freedom vehicle model;

[0011] A feedback dissipation Hamiltonian system for a vehicle trajectory tracking model is established using the orthogonal decomposition method and the state feedback method.

[0012] Based on the aforementioned feedback dissipative Hamiltonian system, and in conjunction with the Hamiltonian robust control method, a generalized Hamiltonian robust controller is designed.

[0013] As one possible implementation of this embodiment, the two-degree-of-freedom vehicle model is as follows:

[0014]

[0015] Where m is the total mass of the vehicle; z Let y be the vehicle's moment of inertia about the z-axis; a and b be the distances from the vehicle's center of mass to the front and rear axles, respectively; and y be the vehicle's lateral displacement. This refers to the vehicle's heading angle; The vehicle's lateral speed; v is the yaw rate of the vehicle. x C represents the longitudinal speed of the vehicle. αf Cαr These are the lateral stiffness of the front and rear wheels, respectively; δ f This refers to the steering angle of the vehicle's front wheels.

[0016] As one possible implementation of this embodiment, the vehicle trajectory tracking model is as follows:

[0017]

[0018] Where A, B, and C are coefficient matrices; For state variables; u = δ f ω is the control variable; e is the interference of the reference path information on the system; ω is the lateral error. For heading error;

[0019]

[0020] Let be the desired vehicle yaw rate.

[0021] As one possible implementation of this embodiment, the feedback dissipation Hamiltonian system of the vehicle trajectory tracking model is:

[0022]

[0023] Where, x∈R 4 ;u∈R;ω∈R 2 It is interference; y∈R 2 This is the output; z∈R is the evaluation signal; r(x) is the full-rank weight matrix. It is an antisymmetric matrix; R1()≥0 is a symmetric positive semi-definite matrix.

[0024] As one possible implementation of this embodiment, the generalized Hamilton robust controller is:

[0025]

[0026] In the formula, λ is the interference suppression level; r(x) is the full-rank weight matrix.

[0027] As one possible implementation of this embodiment, the robust control method for intelligent vehicle trajectory tracking further includes the following steps:

[0028] The generalized Hamilton robust controller is derived and verified using Lyapunov functions.

[0029] As one possible implementation of this embodiment, the derivation and verification of the generalized Hamiltonian robust controller using Lyapunov functions includes:

[0030] The Hamilton-Jacobian inequality for a generalized Hamilton robust controller is:

[0031]

[0032] ≤0

[0033] when When the L2 norm gain of the closed-loop system is no greater than λ, the stability of the generalized Hamilton robust controller can be guaranteed.

[0034] When ω = 0

[0035]

[0036] Therefore, the closed-loop system converges to the largest invariant set contained in the following set:

[0037]

[0038] From the above formula, it can be seen that when hour, Combining the formula for calculating the lateral error and the equation of the intelligent vehicle trajectory tracking control system, we obtain x1 = x3 = 0. According to the LaSalle invariance principle, the closed-loop system equation is asymptotically stable when ω = 0. Therefore, the H∞ control problem of the intelligent vehicle trajectory tracking dissipative Hamiltonian system equation can be completed by the generalized Hamiltonian robust controller u.

[0039] Secondly, an embodiment of the present invention provides a robust intelligent vehicle trajectory tracking control system, comprising:

[0040] The model simplification module is used to simplify the target vehicle's vehicle model into a bicycle model, and to establish a two-degree-of-freedom vehicle model.

[0041] The trajectory tracking model building module is used to build a vehicle trajectory tracking model based on a two-degree-of-freedom vehicle model.

[0042] The Hamilton system establishment module is used to establish a feedback dissipation Hamilton system for vehicle trajectory tracking models using the orthogonal decomposition method and the state feedback method.

[0043] The robust controller design module is used to design a generalized Hamilton robust controller based on the feedback dissipation Hamilton system and in conjunction with the Hamilton robust control method.

[0044] Thirdly, an embodiment of the present invention provides a computer device including a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the computer device is running, the processor communicates with the memory via the bus, and the processor executes the machine-readable instructions to perform the steps of any of the above-described robust control methods for intelligent vehicle trajectory tracking.

[0045] Fourthly, embodiments of the present invention provide a storage medium storing a computer program, which, when executed by a processor, performs the steps of any of the above-described robust control methods for intelligent vehicle trajectory tracking.

[0046] The technical solutions of the embodiments of the present invention can have the following beneficial effects:

[0047] This invention achieves Hamiltonian feedback dissipation through a state feedback controller, ensuring the convergence of the intelligent vehicle trajectory tracking control system. Furthermore, the designed Hamiltonian robust controller significantly improves the stability and accuracy of intelligent vehicle trajectory tracking. Compared to existing technologies, this invention offers advantages such as strong interference suppression, simple parameter adjustment, high real-time performance, low algorithm computational complexity, and independence from precise models, making it promising for broad applications.

[0048] This invention utilizes orthogonal decomposition and state feedback methods to obtain the feedback dissipation Hamiltonian of the vehicle trajectory tracking control system. Combined with the Hamiltonian robust control method, the designed Hamiltonian robust controller significantly reduces algorithm complexity, improves computational efficiency, and reduces the error of the controlled vehicle tracking the reference trajectory compared to sliding mode controllers and LQR controllers. Attached Figure Description

[0049] Figure 1 This is a flowchart illustrating a robust control method for intelligent vehicle trajectory tracking according to an exemplary embodiment;

[0050] Figure 2 This is a schematic diagram illustrating a robust intelligent vehicle trajectory tracking control system according to an exemplary embodiment;

[0051] Figure 3 This is a schematic diagram of a two-degree-of-freedom vehicle model according to an exemplary embodiment;

[0052] Figure 4 This is a schematic diagram of a vehicle trajectory tracking model according to an exemplary embodiment;

[0053] Figure 5 This is a flowchart illustrating an orthogonal decomposition Hamilton implementation according to an exemplary embodiment;

[0054] Figure 6 This is a flowchart illustrating a feedback dissipation Hamilton implementation according to an exemplary embodiment;

[0055] Figure 7 This is a system state response curve diagram illustrating a dissipative Hamilton controller control process according to an exemplary embodiment;

[0056] Figure 8 This is a control signal fluctuation curve diagram illustrating a dissipative Hamilton controller control process according to an exemplary embodiment;

[0057] Figure 9 This is a trajectory tracking control strategy diagram based on generalized Hamiltonian theory, illustrated according to an exemplary embodiment.

[0058] Figure 10 This is a comparison diagram of control algorithm results according to an exemplary embodiment;

[0059] Figure 11 This is a comparison chart illustrating the trajectory tracking effect of an intelligent vehicle according to an exemplary embodiment. Detailed Implementation

[0060] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0061] To clearly illustrate the technical features of this solution, the invention will be described in detail below through specific embodiments and in conjunction with the accompanying drawings. The following disclosure provides many different embodiments or examples for implementing different structures of the invention. To simplify the disclosure of the invention, components and arrangements of specific examples are described below. Furthermore, reference numerals and / or letters may be repeated in different examples. This repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed. It should be noted that the components illustrated in the drawings are not necessarily drawn to scale. Descriptions of well-known components, processing techniques, and processes are omitted in this invention to avoid unnecessarily limiting the invention.

[0062] like Figure 1 As shown in the figure, an embodiment of the present invention provides a robust control method for intelligent vehicle trajectory tracking, comprising the following steps:

[0063] The vehicle model of the target vehicle is simplified into a bicycle model, and a two-degree-of-freedom vehicle model is established.

[0064] A vehicle trajectory tracking model is established based on a two-degree-of-freedom vehicle model;

[0065] A feedback dissipation Hamiltonian system for a vehicle trajectory tracking model is established using the orthogonal decomposition method and the state feedback method.

[0066] Based on the aforementioned feedback dissipative Hamiltonian system, and in conjunction with the Hamiltonian robust control method, a generalized Hamiltonian robust controller is designed.

[0067] As one possible implementation of this embodiment, the two-degree-of-freedom vehicle model is as follows:

[0068]

[0069] Where m is the total mass of the vehicle; I z Let y be the vehicle's moment of inertia about the z-axis; a and b be the distances from the vehicle's center of mass to the front and rear axles, respectively; and y be the vehicle's lateral displacement. This refers to the vehicle's heading angle; The vehicle's lateral speed; v is the yaw rate of the vehicle. x C represents the longitudinal speed of the vehicle. αf C αr These are the lateral stiffness of the front and rear wheels, respectively; δ f This refers to the steering angle of the vehicle's front wheels.

[0070] As one possible implementation of this embodiment, the vehicle trajectory tracking model is as follows:

[0071]

[0072] Where A, B, and C are coefficient matrices; For state variables; u = δ f ω is the control variable; e is the interference of the reference path information on the system; ω is the lateral error. For heading error;

[0073]

[0074] Let be the desired vehicle yaw rate.

[0075] As one possible implementation of this embodiment, the feedback dissipation Hamiltonian system of the vehicle trajectory tracking model is:

[0076]

[0077] Where, x∈R 4 ;u∈R;ω∈R 2 It is interference; y∈R 2 This is the output; z∈R is the evaluation signal; r(x) is the full-rank weight matrix. It is an antisymmetric matrix; R1(x)≥0 is an antisymmetric positive semidefinite matrix.

[0078] As one possible implementation of this embodiment, the generalized Hamilton robust controller is:

[0079]

[0080] In the formula, λ is the interference suppression level; r(x) is the full-rank weight matrix.

[0081] As one possible implementation of this embodiment, the robust control method for intelligent vehicle trajectory tracking further includes the following steps:

[0082] The generalized Hamilton robust controller is derived and verified using Lyapunov functions.

[0083] As one possible implementation of this embodiment, the derivation and verification of the generalized Hamiltonian robust controller using Lyapunov functions includes:

[0084] The Hamilton-Jacobian inequality for a generalized Hamilton robust controller is:

[0085]

[0086] when When the L2 norm gain of the closed-loop system is no greater than λ, the stability of the generalized Hamilton robust controller can be guaranteed.

[0087] When ω = 0

[0088]

[0089] Therefore, it can be seen that the closed-loop system converges to the largest invariant set contained in the following set:

[0090]

[0091] As can be seen from the above formula, when hour, Combining the formula for calculating lateral error and the equation of the intelligent vehicle trajectory tracking control system, we can derive x1 = x3 = 0. By the LaSalle invariance principle, the closed-loop system is asymptotically stable when ω = 0. Therefore, the H∞ control problem of the dissipative Hamiltonian system for intelligent vehicle trajectory tracking can be solved by the generalized Hamiltonian robust controller u.

[0092] This invention aims to solve various problems in trajectory tracking control of intelligent driving vehicles. It integrates a two-degree-of-freedom vehicle dynamics model and a trajectory tracking model to establish a trajectory tracking control system describing the vehicle's motion state. Based on Hamiltonian theory, it uses orthogonal decomposition and state feedback methods to model the trajectory tracking control system, thereby obtaining a feedback dissipation Hamiltonian implementation. Based on the obtained vehicle feedback dissipation Hamiltonian system, and combined with the Hamiltonian robust control method, a generalized Hamiltonian robust controller is designed using the front wheel angle as the control output and the lateral error derivative and yaw rate error as state inputs. This generalized Hamiltonian robust controller is then applied to the trajectory tracking control of intelligent vehicles, thereby achieving trajectory tracking control of intelligent vehicles and improving their stability and accuracy.

[0093] This invention describes the vehicle's motion state using orthogonal decomposition based on the vehicle trajectory tracking control system equations, and then employs a state feedback method to find an appropriate state feedback control law, thereby obtaining a vehicle trajectory tracking dissipative Hamiltonian system and achieving stable control of the vehicle system. The control algorithm implemented in this invention uses generalized Hamiltonian robust control theory, employing feedback control of the intelligent vehicle's state to enable it to move accurately along the desired trajectory.

[0094] This invention achieves Hamiltonian feedback dissipation through a state feedback controller, ensuring the convergence of the intelligent vehicle trajectory tracking control system. Furthermore, the designed Hamiltonian robust controller significantly improves the stability and accuracy of intelligent vehicle trajectory tracking. Compared to existing technologies, this invention offers advantages such as strong interference suppression, simple parameter adjustment, high real-time performance, low algorithm computational complexity, and independence from precise models, making it promising for broad applications.

[0095] like Figure 2 As shown in the figure, an embodiment of the present invention provides a robust intelligent vehicle trajectory tracking control system, comprising:

[0096] The model simplification module is used to simplify the target vehicle's vehicle model into a bicycle model, and to establish a two-degree-of-freedom vehicle model.

[0097] The trajectory tracking model building module is used to build a vehicle trajectory tracking model based on a two-degree-of-freedom vehicle model.

[0098] The Hamilton system establishment module is used to establish a feedback dissipation Hamilton system for vehicle trajectory tracking models using the orthogonal decomposition method and the state feedback method.

[0099] The robust controller design module is used to design a generalized Hamilton robust controller based on the feedback dissipation Hamilton system and in conjunction with the Hamilton robust control method.

[0100] This invention, based on generalized Hamiltonian theory, obtains the Hamiltonian realization of feedback dissipation for a vehicle trajectory tracking control system. Based on reasonable assumptions and simplifications, the actual vehicle model is simplified to a bicycle model, establishing a two-degree-of-freedom vehicle model. Taking full account of the vehicle's kinematic characteristics during driving, a vehicle trajectory tracking model is established to reflect the actual driving conditions, ultimately yielding the vehicle trajectory tracking control system. The orthogonal decomposition method and state feedback method are used to obtain the Hamiltonian realization of feedback dissipation for the vehicle trajectory tracking control system.

[0101] This invention presents a robust control method for intelligent vehicle trajectory tracking based on generalized Hamiltonian theory. First, based on the feedback dissipation Hamiltonian system obtained above, and combined with the Hamiltonian robust control method, a generalized Hamiltonian robust controller for intelligent vehicle trajectory tracking is designed. Then, to study the stability of the autonomous driving system, the Lyapunov function is selected for derivation and verification. This invention uses the designed Hamiltonian robust controller to control the vehicle, thereby reducing the error between the actual trajectory and the reference trajectory during the intelligent vehicle's movement.

[0102] The implementation of this invention mainly includes the following processes: establishing a vehicle dynamics model, establishing a vehicle trajectory tracking model, constructing a vehicle trajectory tracking control system, implementing feedback dissipation Hamiltonian, and analyzing digital simulation results.

[0103] 1. For example Figure 3 The diagram shows a schematic of a two-degree-of-freedom dynamic model of an intelligent vehicle. This dynamic model mainly reflects the lateral dynamics and yaw motion characteristics of the vehicle. When the tires are in the linear region, it can better reflect the lateral dynamics characteristics of the vehicle. Based on reasonable assumptions and simplifications, the actual vehicle model is simplified to a bicycle model, that is, the steering system, suspension system, and dynamic effects of the vehicle are ignored; it is assumed that the longitudinal velocity of the vehicle is constant. According to Newton's second law and the torque equation, a two-degree-of-freedom model of the vehicle is established. The dynamic equations of the vehicle can be described as follows:

[0104] ma y =F yf cosδ f +F yr

[0105]

[0106] In the formula, m is the total mass of the vehicle; ay F is the lateral acceleration of the vehicle. yf F yr These are the lateral forces acting on the front and rear wheels, respectively; I z Let be the vehicle's moment of inertia about the z-axis; γ be the vehicle's yaw rate; a and b be the distances from the vehicle's center of mass to the front and rear axles, respectively; δ f This refers to the steering angle of the vehicle's front wheels.

[0107] Based on the tire force expression formula, and combined with the velocity composition and decomposition of rigid body kinematics, the following vehicle lateral acceleration a can be obtained. y The relationship between the lateral displacement y and the lateral stiffness C of the front and rear wheels. αf and C αr The specific expression.

[0108]

[0109] In the formula, v is the yaw rate of the vehicle. y v is the lateral velocity of the vehicle. x C represents the longitudinal velocity; αf C αr These are the lateral stiffness of the front and rear wheels, respectively; α f α r These are the front and rear wheel slip angles, respectively; δ f This refers to the steering angle of the vehicle's front wheels.

[0110] Based on the above formula, the equations for the two-degree-of-freedom dynamics model of the vehicle are as follows:

[0111]

[0112] 2. For example Figure 4 The diagram illustrates a schematic of an intelligent vehicle trajectory tracking model. This trajectory tracking model primarily characterizes the vehicle's motion characteristics during actual driving. In the geodetic coordinate system XOY, the Y-axis is the projection of the vehicle's position onto the Y-axis of the absolute coordinate system, and the X-axis is the projection of the vehicle's position onto the X-axis of the absolute coordinate system, conforming to the right-hand rule. The dynamic coordinate system xoy is fixed at the vehicle's center of gravity; its x-axis is in the same direction as the vehicle's longitudinal axis, and its y-axis is in the same direction as the vehicle's transverse axis, with positive directions from right to left.

[0113] To achieve the trajectory tracking control objective, this invention... Figure 4 The intelligent vehicle trajectory tracking model shown was analyzed and its formulas were derived, resulting in the following equations:

[0114]

[0115] In the formula, For heading error; The desired vehicle heading angle; ρ is the vehicle's yaw rate; e is the curvature of the desired trajectory; v is the lateral error; ρ is the yaw rate of the vehicle; ρ is the curvature of the desired trajectory; e is the lateral error; v y This represents the vehicle's lateral speed.

[0116] set up u = δ f After simplification and rearrangement, the final equation for the vehicle trajectory tracking control system is:

[0117]

[0118] Where A, B, and C are coefficient matrices; x is the state variable; u is the control variable; ω is the interference of the reference path information on the system; and e is the lateral error. For heading error;

[0119]

[0120] Let be the desired vehicle yaw rate.

[0121] 3. For example Figure 5 The diagram shows a flowchart of the orthogonal decomposition Hamiltonian implementation for an intelligent vehicle trajectory tracking control system. The orthogonal decomposition method is independent of g(x) and can provide a more efficient and convenient Hamiltonian implementation for any system. The specific implementation steps are as follows:

[0122] First, the vehicle system equations are rewritten in the following form:

[0123]

[0124] make

[0125]

[0126] Secondly, the Hamilton function is selected:

[0127]

[0128] Its partial derivative is expressed as:

[0129]

[0130] Construct the isosurface of H(x), and at any point x≠0, apply f(x) along the gradient direction. Decomposed along the tangential direction, we have:

[0131] f(x)=f gd (x)+f td (x)

[0132] In the formula,

[0133]

[0134]

[0135] but:

[0136]

[0137]

[0138] In the formula, S gd = (x2(a1x2+a2x3+a3x4))+(x4(a4x2+a5x3+a6x4))+x1x2+x3x4); I4 is a 4×4 identity matrix.

[0139] Then when x≠0,

[0140]

[0141] Ultimately, the Hamilton intelligent vehicle trajectory tracking control system is implemented as follows:

[0142]

[0143] 4. For example Figure 6 The diagram shows a flowchart of the Hamiltonian implementation of feedback dissipation in an intelligent vehicle trajectory tracking control system. The main idea behind feedback dissipation is to find an appropriate state feedback to offset the non-dissipative terms (semi-positive definite or positive definite parts) in the structure matrix. In other words, the structure matrix is ​​modified through feedback without changing the Hamiltonian function to achieve dissipation. This control concept is similar to bang-bang control; although the control function changes continuously, its Lyapunov function remains the same, H(x). Therefore, the intelligent vehicle system can be made to converge to the origin. The specific implementation steps are as follows:

[0144] In R 4 In the middle, consider equation L g1 H = 0 is denoted as Σ1, representing a hypersurface. Let x ∈ R. 4 It is a given point. If Then L g1 H≠0. At this point, S(x) can be decomposed into:

[0145] S(x) = -R1(x) + R2(x)

[0146] Select:

[0147]

[0148]

[0149] In the formula, R1>0, and R2 is symmetric;

[0150] Take control rate:

[0151]

[0152] Applying controller u1 to the orthogonal decomposition of the intelligent vehicle trajectory tracking control system in Hamilton implementation, we obtain:

[0153]

[0154] In the formula,

[0155]

[0156] because It is antisymmetric, and R1>0, so the state feedback Hamiltonian implementation of the intelligent vehicle trajectory tracking control system is strictly dissipative.

[0157] because,

[0158]

[0159] Therefore, the intelligent vehicle trajectory tracking control system has a strictly dissipative Hamiltonian feedback implementation. Thus, the Hamiltonian feedback dissipation implementation of the intelligent vehicle trajectory tracking control system can be achieved through the state feedback controller u1.

[0160] 5. For example Figure 7 and Figure 8 The figure shows a digital simulation of the dissipative controller control process. Figure 7 It is the response curve of the system state variables. Figure 8 This is the fluctuation curve of the corresponding control signal. Here, let the reference input v = 0, and the initial value x0 = (0.01 0.1 0.01 0.1). T According to the simulation results shown in the figure, the various state variables of the system fluctuate significantly during the time period of 0-2 seconds. The control signal shows a trend of first decreasing and then increasing, reaching its lowest point at approximately 0.15 seconds. At 2 seconds, state variable x3 gradually approaches 0, and state variable x4 is approximately 0, reaching a stable state first. After 2 seconds, the control signal u1 gradually approaches stability, and the fluctuations in the system's state variables gradually decrease. At approximately 7 seconds, state variables x1 and x2 also approach 0. Clearly, after 7 seconds, all the system's state variables are approximately 0, and the value of controller u1 is also 0, which is consistent with the theoretical expectation. The simulation results show that the designed state feedback controller is effective.

[0161] This invention relates to a robust control method for intelligent vehicle trajectory tracking based on generalized Hamiltonian theory, comprising: designing a generalized Hamiltonian robust controller, verifying the stability of the controller and its system, designing a trajectory tracking control strategy, and using Carsim and Simulink to build a corresponding model for joint simulation.

[0162] 1. Design of a generalized Hamiltonian robust controller. Based on the obtained vehicle trajectory tracking dissipation Hamiltonian system:

[0163]

[0164] Where, x∈R 4 ;u∈R;ω∈R 2 It is interference; y∈R 2 This is the output; z∈R is the evaluation signal; r(x) is the full-rank weight matrix; R1() is an antisymmetric matrix; R1()≥0 is a symmetric positive semi-definite matrix. Here, it is assumed that H(x) attains a local minimum at the equilibrium point x0.

[0165] The design problem of the H∞ controller of this system is: for a given suppression level λ>0, find a suitable state feedback control law u=α(x)((0)=0) such that the L2 gain of the closed-loop system (from ω to z) is not greater than λ.

[0166] Its state feedback control law design is as follows:

[0167]

[0168] In the formula, λ is the interference suppression level; r(x) is the full-rank weight matrix.

[0169] 2. Stability of the controller and its system. The Lyapunov function V(x) = H() ≥ 0 is selected, specifically including:

[0170] The Hamilton-Jacobian inequality for an intelligent vehicle trajectory tracking control system is as follows:

[0171]

[0172] Therefore, when When the L2 norm gain of the closed-loop system is no greater than λ, the stability of the control system can be guaranteed.

[0173] When ω = 0

[0174]

[0175] Therefore, it can be seen that the closed-loop system converges to the largest invariant set contained in the following set:

[0176]

[0177] As can be seen from the above formula, when hour, Combining the formula for calculating lateral error and the equation of the intelligent vehicle trajectory tracking control system, we can derive x1 = x3 = 0. By the LaSalle invariance principle, the closed-loop system is asymptotically stable when ω = 0. Therefore, the H∞ control problem of the dissipative Hamiltonian system for intelligent vehicle trajectory tracking can be solved by the generalized Hamiltonian robust controller u.

[0178] 3. For example Figure 9 As shown, a diagram illustrating the intelligent vehicle trajectory tracking control strategy based on generalized Hamiltonian theory is presented. In the diagram, y ref The lateral position of the vehicle's desired path. Let e ​​be the vehicle's desired heading angle, and 'e' be the lateral error. For heading error, This refers to the lateral velocity error. For the yaw rate error, δ f v is the steering angle of the vehicle's front wheels. y v is the lateral velocity of the vehicle. x Let γ be the vehicle's longitudinal velocity, γ be the vehicle's yaw rate, y be the vehicle's lateral displacement, x be the vehicle's longitudinal displacement, and ρ be the curvature of the desired trajectory. Let β be the vehicle's heading angle and β be the vehicle's sideslip angle. Based on the preset reference trajectory and the output of the Carsim vehicle model, the required state variables and their errors are obtained. A designed Hamiltonian robust controller is used to ensure that the derivatives of the lateral and heading errors approach zero during trajectory tracking. This provides the control input for the required front wheel steering angle of the vehicle system, ultimately achieving good trajectory tracking performance.

[0179] The following specific examples illustrate the solution described in this invention:

[0180] 1) According to Figure 9 The simulation of the intelligent vehicle trajectory tracking control strategy shown is as follows, where the desired trajectory is a double-line-change trajectory, and the desired lateral position y of the path is... ref and expected heading angle The expression is as follows:

[0181]

[0182]

[0183] In the formula, z1 = 0.095*(X) ref -60)-1.2;z1=0.095*(Xref -120)-1.2; dm1=25; dm2=25; dn1=3.6; dn2=3.6;

[0184] 2) The system performance indicators of this invention include the lateral error and heading deviation between the actual vehicle trajectory and the reference trajectory. The smaller these errors are, the better the vehicle trajectory tracking control effect.

[0185] 3) Specific implementation steps and key parameter settings for the intelligent vehicle trajectory tracking system and robust control method based on generalized Hamiltonian theory. Details are as follows:

[0186] 1. System Modeling. Write Matlab code to build the system model, providing data support for the subsequently designed controller.

[0187] (1) Establish a trajectory tracking control system that describes the vehicle's motion state. This includes integrating a two-degree-of-freedom vehicle dynamics model and a trajectory tracking model to determine the system's control objectives and the variables that need to be controlled, including the vehicle's state variables and control variables.

[0188] (2) Hamiltonian implementation of the vehicle system. The Hamiltonian function of the system is determined according to the generalized Hamiltonian principle. The Hamiltonian implementation of the intelligent vehicle trajectory tracking control system is orthogonally decomposed, and feedback control is further added. A state feedback controller is designed to complete the feedback dissipation Hamiltonian implementation of the intelligent vehicle trajectory tracking control system, thereby achieving stable control of the system.

[0189] (3) Digital simulation verification. Based on the designed state feedback controller, verify whether the obtained intelligent vehicle feedback dissipation Hamilton system is stable.

[0190] 2. Design of a Hamilton Robust Controller. Combining the Hamilton robust control method, the front wheel steering angle is used as the control output, and the lateral velocity error and yaw rate error are used as state inputs. A robust controller based on the generalized Hamiltonian principle is designed. Stability analysis is performed using Lyapunov functions and the LaSalle invariant principle to verify the stability of the Hamilton robust controller. This enables trajectory tracking control of the vehicle, enhances the system's robustness, and improves control accuracy and stability.

[0191] 3. Implement co-simulation of the algorithm using both Simulink and Carsim tools. According to... Figure 9The intelligent vehicle trajectory tracking control strategy shown uses the S-Function module and other common modules in Simulink to build the simulation environment, such as the reference trajectory module, error calculation module, and controller control module. In Carsim, necessary vehicle parameters are set, such as input / output modules, overall vehicle parameters, and road environment settings.

[0192] 4. Data processing and analysis to determine optimal parameters. Based on theoretical analysis and a large amount of experimental data, the interference suppression level λ = 8 and the weight matrix r = 0.05 were finally determined in the Hamilton robust trajectory tracking controller.

[0193] 5. Algorithm Comparison and Analysis. Simulation comparison results with sliding mode controllers and LQR controllers are as follows: Figure 10 As shown in the figure, the front wheel steering angle of the three controllers is compared at different vehicle speeds. It can be seen from the figure that, under all vehicle speed conditions, the Hamilton control algorithm outperforms sliding mode control and LQR control in controlling the front wheel steering angle. As the vehicle speed increases, the control effect of LQR gradually approaches that of Hamilton control, and at 72 km / h, the difference in control effect between the two is not significant. Calculations show that, compared to sliding mode control, the optimization effect of the Hamilton control algorithm gradually increases from 4.53% to 13.87% with increasing vehicle speed, and its RMS value optimization effect is also between 4.20% and 7.67%. This indicates that the Hamilton controller has a better control effect and a smoother front wheel steering angle curve.

[0194] 6. Trajectory tracking and comparison. For example... Figure 11 As shown, at three different vehicle speeds, the Hamilton controller achieves a more accurate tracking performance of the reference trajectory in the double lane change scenario. Analysis of the statistical data reveals that at a vehicle speed of 36 km / h, the peak lateral displacement error using the Hamilton controller is 0.0514 m, representing reductions of 82.27% and 67.90% compared to the SMC and LQR controllers, respectively. This indicates that the Hamilton robust controller possesses higher tracking accuracy. At vehicle speeds of 54 km / h and 72 km / h, compared to the SMC and LQR controllers, the Hamilton controller shows optimization effects of 41.24%–75.05% on the peak lateral displacement error and 43.28%–75.68% on error fluctuation. Therefore, it can be seen that the Hamilton robust controller demonstrates excellent optimization effects in terms of trajectory tracking accuracy and trajectory smoothness under low, medium, and medium-to-high speed conditions.

[0195] It should be noted that in the above function (equation), there is one point directly above the parameter, which represents the first derivative, and two points, which represent the second derivative.

[0196] An embodiment of the present invention provides a computer device including a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the device is running, the processor communicates with the memory via the bus, and the processor executes the machine-readable instructions to perform the steps of any of the above-described robust control methods for intelligent vehicle trajectory tracking.

[0197] Specifically, the aforementioned memory and processor can be general-purpose memory and processor, without any specific limitations. When the processor runs the computer program stored in the memory, it can execute the aforementioned robust control method for intelligent vehicle trajectory tracking.

[0198] Those skilled in the art will understand that the structure of the computer device does not constitute a limitation on the computer device, and may include more or fewer components than shown in the figure, or combine some components, or split some components, or have different component arrangements.

[0199] In some embodiments, the computer device may further include a touchscreen for displaying a graphical user interface (e.g., an application launch screen) and receiving user actions on the graphical user interface (e.g., launching an application). Specifically, the touchscreen may include a display panel and a touch panel. The display panel may be configured as an LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode), or similar type. The touch panel can collect user touch or non-touch operations on or near it and generate pre-set operation instructions, such as user actions using fingers, styluses, or any suitable object or accessory on or near the touch panel. Additionally, the touch panel may include a touch detection device and a touch controller. The touch detection device detects the user's touch orientation and posture, and detects the signals generated by the touch operation, transmitting the signals to the touch controller. The touch controller receives touch information from the touch detection device, converts it into information that the processor can process, sends it to the processor, and can also receive and execute commands from the processor. Furthermore, touch panels can be implemented using various types of sensors, including resistive, capacitive, infrared, and surface acoustic wave sensors, as well as any future technologies. Moreover, the touch panel can cover the display panel. Users can operate on or near the touch panel, which is covered by the graphical user interface displayed on the display panel. After detecting the operation on or near the touch panel, the touch panel transmits it to the processor to determine the user input. The processor then responds to the user input by providing corresponding visual output on the display panel. Additionally, the touch panel and display panel can be implemented as two separate components or integrated together.

[0200] Corresponding to the above application startup method, this embodiment of the invention also provides a storage medium storing a computer program, which, when run by a processor, executes the steps of any of the above-described intelligent vehicle trajectory tracking robust control methods.

[0201] The application launch device provided in this application embodiment can be specific hardware on the device or software or firmware installed on the device. The device provided in this application embodiment has the same implementation principle and technical effects as the foregoing method embodiments. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the foregoing method embodiments. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can all be referred to the corresponding processes in the above method embodiments, and will not be repeated here.

[0202] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0203] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of modules is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interface, and the indirect coupling or communication connection of the apparatus or modules may be electrical, mechanical, or other forms.

[0204] The modules described as separate components may or may not be physically separate. Similarly, the components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0205] In addition, the functional modules in the embodiments provided in this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0206] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0207] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0208] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0209] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A robust control method for intelligent vehicle trajectory tracking, characterized in that, Includes the following steps: The target vehicle model is simplified to a bicycle model, and a two-degree-of-freedom vehicle model is established. The state variables of the two-degree-of-freedom vehicle model include lateral displacement y and heading angle. Lateral velocity yaw rate The parameters include the front and rear wheel lateral stiffness C. αf C αr With the vehicle's longitudinal speed v x Related items; A vehicle trajectory tracking model is established based on a two-degree-of-freedom vehicle model; A feedback dissipation Hamiltonian system for a vehicle trajectory tracking model is established using the orthogonal decomposition method and the state feedback method. Based on the aforementioned feedback dissipative Hamiltonian system and combined with the Hamiltonian robust control method, a generalized Hamiltonian robust controller is designed. The output of the generalized Hamiltonian robust controller is the front wheel steering angle δ. f The controller parameter λ represents the interference suppression level for vehicle trajectory tracking, and r(x) is the weight matrix for the derivatives of the lateral error and the heading error. The feedback dissipation Hamiltonian system of the vehicle trajectory tracking model is: Where, x∈R 4 ;u∈R;ω∈R 2 It is interference; y∈R 2 This is the output; z∈R is the evaluation signal; r(x) is the full-rank weight matrix. It is an antisymmetric matrix; R1(x)≥0 is a symmetric positive semi-definite matrix; The generalized Hamilton robust controller is: In the formula, λ is the interference suppression level; r(x) is the full-rank weight matrix.

2. The robust control method for intelligent vehicle trajectory tracking according to claim 1, characterized in that, The two-degree-of-freedom vehicle model is as follows: Where m is the total mass of the vehicle; I z Let y be the vehicle's moment of inertia about the z-axis; a and b be the distances from the vehicle's center of mass to the front and rear axles, respectively; and y be the vehicle's lateral displacement. This refers to the vehicle's heading angle; The vehicle's lateral speed; v is the yaw rate of the vehicle. x C represents the longitudinal speed of the vehicle. αf C αr These are the front and rear wheel lateral stiffness, respectively; δ f This refers to the steering angle of the vehicle's front wheels.

3. The robust control method for intelligent vehicle trajectory tracking according to claim 2, characterized in that, The vehicle trajectory tracking model is as follows: Where A, B, and C are coefficient matrices; For state variables; u = δ f ω is the control variable; e is the interference of the reference path information on the system; ω is the lateral error. For heading error; Let be the desired vehicle yaw rate.

4. The robust control method for intelligent vehicle trajectory tracking according to any one of claims 1-3, characterized in that, It also includes the following steps: The generalized Hamilton robust controller is derived and verified using Lyapunov functions.

5. The robust control method for intelligent vehicle trajectory tracking according to claim 4, characterized in that, The derivation and verification of the generalized Hamiltonian robust controller using Lyapunov functions includes: The Hamilton-Jacobian inequality for a generalized Hamilton robust controller is: when When the L2 norm gain of the closed-loop system is no greater than λ, the stability of the generalized Hamilton robust controller can be guaranteed. When ω = 0 therefore, The closed-loop system converges to the maximal invariant set contained in the following set: From the above formula, it can be seen that when When x2 = x4 = 0, Combining the formula for calculating the lateral error and the equation of the intelligent vehicle trajectory tracking control system, we obtain x1 = x3 = 0. According to the LaSalle invariance principle, the closed-loop system equation is asymptotically stable when ω = 0. Therefore, the H∞ control problem of the intelligent vehicle trajectory tracking dissipative Hamiltonian system equation is completed by the generalized Hamiltonian robust controller u.

6. A robust control system for intelligent vehicle trajectory tracking, characterized in that, include: The model simplification module is used to simplify the target vehicle model into a bicycle model, establishing a two-degree-of-freedom vehicle model. The state variables of the two-degree-of-freedom vehicle model include lateral displacement y and heading angle. Lateral velocity yaw rate The parameters include the front and rear wheel lateral stiffness C. αf C αr With the vehicle's longitudinal speed v x Related items; The trajectory tracking model building module is used to build a vehicle trajectory tracking model based on a two-degree-of-freedom vehicle model. The Hamilton system establishment module is used to establish a feedback dissipation Hamilton system for vehicle trajectory tracking models using the orthogonal decomposition method and the state feedback method. The robust controller design module is used to design a generalized Hamiltonian robust controller based on the feedback dissipation Hamiltonian system and the Hamiltonian robust control method. The output of the generalized Hamiltonian robust controller is the front wheel steering angle δ. f The controller parameter λ represents the interference suppression level for vehicle trajectory tracking, and r(x) is the weight matrix for the derivatives of the lateral error and the heading error. The feedback dissipation Hamiltonian system of the vehicle trajectory tracking model is: Where, x∈R 4 ;u∈R;ω∈R 2 It is interference; y∈R 2 This is the output; z∈R is the evaluation signal; r(x) is the full-rank weight matrix. It is an antisymmetric matrix; R1(x)≥0 is a symmetric positive semi-definite matrix; The generalized Hamilton robust controller is: In the formula, λ is the interference suppression level; r(x) is the full-rank weight matrix.

7. A computer device, characterized in that, The device includes a processor, a memory, and a bus. The memory stores machine-readable instructions that the processor can execute. When the computer device is running, the processor communicates with the memory via the bus, and the processor executes the machine-readable instructions to perform the steps of the robust control method for intelligent vehicle trajectory tracking as described in any one of claims 1-5.

8. A storage medium, characterized in that, The storage medium stores a computer program that, when executed by a processor, performs the steps of the robust control method for intelligent vehicle trajectory tracking as described in any one of claims 1-5.

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