Automatic driving vehicle trajectory tracking method and device based on data driving model predictive control

Abstract

The invention discloses an automatic driving vehicle trajectory tracking method and device based on data driving model predictive control, and the method comprises the steps: carrying out the discretization of a continuous vehicle transverse and longitudinal error model, obtaining a discrete vehicle transverse and longitudinal error model, and selecting the control input and system output; collecting a plurality of data as prior input data based on control input, and constructing a Hankel matrix; applying the prior input data to a discrete vehicle transverse and longitudinal error model, collecting correspondingly generated prior output data based on system output, and constructing a Hankel matrix; on the basis of theoretical knowledge of a behavior system, a Hankel matrix constructed by priori input and output data is used for representing dynamic characteristics of the system; on the basis of data representation of a nominal system model corresponding to the discrete vehicle transverse and longitudinal error model, a cost function is constructed, and then an optimization problem of data-driven model prediction control is constructed; and solving an optimization problem, and applying the obtained optimal control to the system to complete control. According to the method, the system can be driven to run along the expected trajectory while system constraints are met, and high precision and robustness of trajectory tracking are ensured.

Description

Technical Field

[0001] The present invention relates to the field of autonomous driving technology, and in particular to a method, device, terminal equipment and computer-readable storage medium for tracking trajectory of an autonomous driving vehicle based on data-driven model predictive control. Background Art

[0002] The autonomous driving vehicle system consists of multiple modules such as perception, planning, decision-making and control, among which the control module is one of the most critical modules. With the continuous development of autonomous driving technology, trajectory tracking has become a key technology in the control module. The existing trajectory tracking methods mainly include proportional-integral-derivative (PID) control, model predictive control (MPC), adaptive control, sliding mode control (SMC), linear quadratic regulator (LQR) and other methods. These different trajectory tracking methods have their own advantages and disadvantages. Among them, model predictive control is a commonly used advanced control method in current trajectory tracking. Model predictive control predicts the motion trajectory of the vehicle in the future by solving an optimization problem and calculates the optimal control input to minimize the trajectory error. Compared with other trajectory tracking methods, the main advantage of model predictive control is that it can achieve rolling optimization and explicit processing constraints. However, traditional model predictive control relies on an accurate vehicle dynamics model, which has many parameters and requires parameter identification through experiments; in addition, the complexity and uncertainty in the actual road environment make it often show poor robustness when facing emergencies, external interference or incomplete system models. Summary of the invention

[0003] In order to address the deficiencies of the above-mentioned prior art, the present invention provides a method, apparatus, terminal device and computer-readable storage medium for autonomous driving vehicle trajectory tracking based on data-driven model predictive control, which avoids the precise modeling requirements of traditional model predictive control for the system, directly utilizes limited offline data and behavioral system theory, and calculates the optimal and safe control strategy through real-time feedback, so as to drive the system along the desired trajectory while satisfying system constraints, thereby ensuring high accuracy and robustness of trajectory tracking.

[0004] The first object of the present invention is to provide a method for autonomous driving vehicle trajectory tracking based on data-driven model predictive control.

[0005] The second object of the present invention is to provide an autonomous driving vehicle trajectory tracking device based on data-driven model predictive control.

[0006] The third object of the present invention is to provide a terminal device.

[0007] A fourth object of the present invention is to provide a computer-readable storage medium.

[0008] The first object of the present invention can be achieved by adopting the following technical solutions:

[0009] A method for tracking trajectory of an autonomous driving vehicle based on data-driven model predictive control, the method comprising:

[0010] Discretize the continuous vehicle lateral and longitudinal error model to obtain a discrete vehicle lateral and longitudinal error model; select the control input and system output based on the discrete vehicle lateral and longitudinal error model;

[0011] Based on the control input, multiple input data are collected and used as a set of prior input data; a corresponding Hankel matrix is ​​constructed according to the prior input data and it is confirmed that the collected prior input data is continuously excited;

[0012] Apply the prior input data to the discrete vehicle lateral and longitudinal error model, collect the corresponding prior output data based on the system output; construct the corresponding Hankel matrix based on the prior output data;

[0013] Based on the theoretical knowledge of behavioral systems, the dynamic characteristics of the system are represented by the Hankel matrix constructed by the prior input data and the Hankel matrix constructed by the prior output data;

[0014] Based on the data representation of the nominal system model corresponding to the discrete vehicle lateral and longitudinal error model, a cost function is constructed; based on the cost function, an optimization problem of data-driven model predictive control is constructed;

[0015] The optimization problem is solved at every l time step, and the first l input data in the obtained optimal control sequence are applied to the autonomous driving vehicle system to complete the control of l time step; l is a positive integer greater than or equal to 1.

[0016] Furthermore, the discrete vehicle lateral and longitudinal error model is:

[0017]

[0018] in:

[0019] I is the identity matrix;

[0020]

[0021]

[0022] In the formula, x(k+1) and x(k) represent the states of the system at time k+1 and k respectively, u(k) represents the control input at time k, and y(k) represents the system output at time k; are coefficient matrices; m is the vehicle mass; I zis the moment of inertia of the vehicle around the z axis in the vehicle coordinate system, l f and l r Respectively front wheelbase and rear wheelbase; C af and C ar are the cornering stiffness of the front and rear wheels respectively; V x is the vehicle longitudinal acceleration; Indicates the angular velocity required for the vehicle body to turn the desired angle;

[0023] According to the discrete vehicle lateral and longitudinal error model, the front wheel steering angle δ and acceleration a of the automatic driving vehicle are selected as control inputs to select input data; the lateral error y is selected e , lateral error rate Heading error θ e , heading error rate Longitudinal error x e and longitudinal error rate As system output to select the output data.

[0024] Furthermore, the Hankel matrix constructed based on the behavioral system theory and the Hankel matrix constructed based on the prior input data and the Hankel matrix constructed based on the prior output data represent the dynamic characteristics of the system, including:

[0025] Ignoring the disturbance term in the discrete vehicle lateral and longitudinal error model

[0026] According to the theoretical knowledge of behavioral systems, any set of input and output data sequences of the system can be expressed as in, is the input vector consisting of the input data sequence, is the output vector consisting of the output data sequence, The input vector and the output vector The augmented vector composed of is the Hankel matrix H constructed from the prior input data L+n (u 0 ) and the Hankel matrix H constructed from the prior output data L+n (y 0 ), the vector g is an NL-l+1 dimensional real vector, N is the number of prior input data and N ≥ (m+1)(L+l+n-1), n ​​is the state dimension of the discrete vehicle lateral and longitudinal error model, m is the input data dimension, L is the prediction domain of the data-driven model predictive control and L>n+l.

[0027] Furthermore, constructing a corresponding Hankel matrix according to the priori input data and confirming that the collected priori input data is continuously excited includes:

[0028] The prior input data is formed into a prior input vector;

[0029] Construct the corresponding Hankel matrix based on the prior input vector;

[0030] If the Hankel matrix is ​​of full row rank, it is confirmed that the collected priori input data is continuously excited; otherwise, a new set of priori input data is collected and it is determined whether it is continuously excited, until the collected priori input data is continuously excited.

[0031] Furthermore, solving the optimization problem at every l time step and applying the first l control input data in the obtained optimal control sequence to the autonomous driving vehicle system to complete the control of l time step includes:

[0032] At the initial time k=0, the optimization problem is solved by taking the l actual control input data and output data before k=0 as initial values ​​to obtain the optimal control input data sequence and the optimal control output data sequence at time 0;

[0033] Select the first l items in the optimal control input data sequence as the control input data for the next l time steps and apply them to the system; then when k=l, use the l actual control input data and output data before k=l as the initial values ​​to solve the optimization problem, select the first l items from the obtained optimal control input data sequence as the control input data for the next l time steps and apply them to the system; solve the optimization problem in this way, apply the first l input data in the obtained optimal control input data sequence to the system to complete the control of l time steps and apply it to the system to ensure the stability of the system and improve the robustness.

[0034] Furthermore, the optimization problem is:

[0035]

[0036] In the formula, Represents the cost function J at time k L (u(k), y(k)) is minimized, and the optimal solution is obtained under the constraints. and g * (k); represents the L+l-dimensional vector composed of the predictive control input data at time k, l time steps backward and L time steps forward, It represents the L+l-dimensional vector composed of the predicted output data of l time steps backward and L time steps forward at time k, H L+l (u 0 ) represents the input data u controlled by the prior 0 The generated L+l-order Hankel matrix, H L+l (y 0) represents the prior output data y 0 The generated L+l order Hankel matrix, u [k-l,k-1] represents the actual control input data of the system l time steps before time k; y [k-l,k-1] represents the actual control output data of the system l time steps before time k; represents the system input data to be predicted at time k; represents the system output data to be predicted at time k; represents the terminal constraint area; is a bounded set of tight constraints on the predictive control input, is a bounded set of tight constraints on the predicted output.

[0037] Furthermore, the cost function is:

[0038]

[0039] In the formula, J L (u(k), y(k)) represents the cost function, u(k) represents the actual control input data of system k at the current moment, and y(k) represents the actual output data of system k at the current moment. represents the predictive control input data for j time steps ahead at the current time k, represents the predicted output data for j time steps forward at the current time k, Represents the expected output data j time steps ahead at the current time k; Is and The augmented state of the composition, represents the vector consisting of the last l prediction control inputs within the current k prediction range, represents the vector composed of the last l predicted outputs within the current k prediction range; R>0 is the control input weight matrix, Q>0 is the output weight matrix, P>0 is the terminal cost weight matrix, λ g is the weight factor, n is the upper bound of noise; g(k) represents the NL-l+1 dimensional real vector composed of the linear combination coefficients of the column vector of the k Hankel matrix at the current moment.

[0040] The second object of the present invention can be achieved by adopting the following technical solutions:

[0041] A device for tracking trajectory of an autonomous driving vehicle based on data-driven model predictive control, the device comprising:

[0042] A module is selected to discretize the continuous vehicle lateral and longitudinal error model to obtain a discrete vehicle lateral and longitudinal error model; based on the discrete vehicle lateral and longitudinal error model, a control input and a system output are selected;

[0043] The acquisition module is used to acquire multiple input data based on the control input and use them as a set of prior input data; construct a corresponding Hankel matrix based on the prior input data and confirm that the acquired prior input data is continuously excited; apply the prior input data to the discrete vehicle lateral and longitudinal error model, and acquire the corresponding prior output data based on the system output; and construct a corresponding Hankel matrix based on the prior output data;

[0044] A representation module is used to represent the dynamic characteristics of the system using the Hankel matrix constructed from the prior input data and the Hankel matrix constructed from the prior output data based on the theoretical knowledge of the behavioral system;

[0045] A construction module is used to construct a cost function based on the data representation of the nominal system model corresponding to the discrete vehicle lateral and longitudinal error model; based on the cost function, an optimization problem of data-driven model predictive control is constructed;

[0046] A control module is used to solve the optimization problem every l time steps, and apply the first l input data in the obtained optimal control sequence to the automatic driving vehicle system to complete the control of l time steps; l is a positive integer greater than or equal to 1.

[0047] The third object of the present invention can be achieved by adopting the following technical solutions:

[0048] A terminal device includes a processor and a memory for storing a program executable by the processor. When the processor executes the program stored in the memory, the above-mentioned autonomous driving vehicle trajectory tracking method based on data-driven model predictive control is implemented.

[0049] The fourth object of the present invention can be achieved by adopting the following technical solutions:

[0050] A computer-readable storage medium stores a program, which, when executed by a processor, implements the above-mentioned autonomous driving vehicle trajectory tracking method based on data-driven model predictive control.

[0051] The present invention has the following beneficial effects compared with the prior art:

[0052] The present invention directly utilizes limited data samples measured offline and theoretical knowledge of behavioral systems to achieve real-time optimal control of unknown systems without complex system identification, thus simplifying the design process of the control system and improving adaptability. It can effectively deal with system uncertainties and external disturbances in dynamic traffic environments, ensuring high-precision trajectory tracking and improving the robustness of the control system. It calculates the optimal and safe control strategy through real-time feedback, quickly drives the vehicle along the desired trajectory while satisfying system constraints, and is suitable for autonomous driving needs in complex dynamic traffic scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0054] Figure 1 This is a flow chart of a method for tracking trajectory of an autonomous driving vehicle based on data-driven model predictive control according to Embodiment 1 of the present invention;

[0055] Figure 2 This is a schematic diagram of the data-driven model predictive control principle of Example 1 of the present invention;

[0056] Figure 3 This is a structural block diagram of an automatic driving vehicle trajectory tracking device based on data-driven model predictive control according to Embodiment 2 of the present invention;

[0057] Figure 4 This is a structural block diagram of the terminal device of embodiment 3 of the present invention. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. It should be understood that the specific embodiments described are only used to explain the present application and are not used to limit the present application.

[0059] Embodiment 1:

[0060] like Figure 1 , 2As shown, this embodiment provides a method for tracking trajectory of an autonomous driving vehicle based on data-driven model predictive control, comprising the following steps:

[0061] S101. Discretize the continuous vehicle lateral and longitudinal error model to obtain a discrete vehicle lateral and longitudinal error model; select a control input and a system output based on the discrete vehicle lateral and longitudinal error model.

[0062] Based on the vehicle dynamics relationship, the continuous vehicle lateral and longitudinal error model can be established as:

[0063]

[0064] Among them: x represents the state of the system, represents the derivative of x; u represents the control input; A and B represent the coefficient matrices; W is the disturbance matrix; and:

[0065]

[0066]

[0067] In the formula, y e represents the lateral error, i.e. the distance between the center of mass of the vehicle and the center line of the lane; is the lateral error rate; θ e is the heading error; is the heading error rate; x e is the longitudinal error; is the longitudinal error rate; δ is the front wheel turning angle, a is the acceleration; m is the vehicle mass; I z is the moment of inertia of the vehicle around the z axis in the vehicle coordinate system, l f and l r Respectively front wheelbase and rear wheelbase; C af and C ar are the cornering stiffness of the front and rear wheels respectively; V x is the vehicle longitudinal acceleration; Indicates the angular velocity required for the vehicle body to turn the desired angle.

[0068] Since the subsequent steps require the use of discrete data, the continuous vehicle lateral and longitudinal error model is discretized using the bilateral discretization method. Assuming the sampling period is 1s, let Then the discrete vehicle lateral and longitudinal error model is obtained as:

[0069]

[0070] In the formula, x(k) represents the state of the system at time k, u(k) represents the control input at time k, y(k) represents the system output at time k, and x(k+1) represents the state of the system at the next time k+1; The specific parameter values ​​are unknown, and the disturbance term The matrix norm of has a constant upper bound Right now

[0071] Based on the discrete vehicle lateral and longitudinal error model (2), the front wheel steering angle δ and acceleration a of the autonomous vehicle are selected as control inputs to select input data u in the subsequent steps; the lateral error y is selected e , lateral error rate Heading error θ e , heading error rate Longitudinal error x e and longitudinal error rate As system output, the output data y is selected in the subsequent steps.

[0072] S102, collecting a plurality of input data based on the control input and using them as a set of priori input data; constructing a corresponding Hankel matrix according to the priori input data and confirming that the collected priori input data is continuously excited.

[0073] According to the control input, collect N input data to form a priori input data set Where N ≥ (m + 1) (L + l + n - 1), where n is the state dimension of the discrete vehicle lateral and longitudinal error model (2), m is the control input dimension, L is the prediction time domain of the data-driven model predictive control, and l is the number of time steps before the current moment.

[0074] The collected prior input data set is formed into a prior input vector u 0 =[u(0) u(1) … u(N-1)]; construct the corresponding L+n order Hankel matrix according to the prior input vector:

[0075]

[0076] And verify whether the Hankel matrix of the prior input vector is full row rank, that is, rank(H L+n (u 0 ))=m(L+n) is true. If so, the Hankel matrix is ​​of full row rank, and the collected prior input data set is continuously excited, that is, it is an ideal data set. If the Hankel matrix is ​​not of full row rank, the prior input data is collected again until the collected prior input data set is continuously excited.

[0077] S103, applying the priori input data to the discrete vehicle lateral and longitudinal error model, collecting the corresponding priori output data based on the system output; and constructing a corresponding Hankel matrix according to the priori output data.

[0078] The collected continuous excitation prior input data set is open-loop applied to the discrete vehicle lateral and longitudinal error model (2), and the corresponding N prior output data sets are collected. The collected prior output data set is combined into a prior output vector y 0 =[y(0) y(1) … y(N-1)]; construct the corresponding L+n order Hankel matrix according to the prior output vector:

[0079]

[0080] S104. Based on the theoretical knowledge of behavioral systems, the Hankel matrix constructed by prior input data and the Hankel matrix constructed by prior output data are used to represent the dynamic characteristics of the system.

[0081] Ignore the disturbance term in the discrete vehicle lateral and longitudinal error model (2) The nominal system model corresponding to the discrete vehicle lateral and longitudinal error model (2) is defined as:

[0082]

[0083] In the formula, is the state of the nominal system, is the nominal system output, is the control input of the nominal system. Model parameters Unknown. According to the knowledge of behavioral system theory, any set of input-output sequences of the nominal system (3) It can be expressed as in is the input vector consisting of the input sequence, is the output vector consisting of the output sequence, The input vector and the output vector The augmented vector composed of is the Hankel matrix H L+n (u 0 ) and H L+n (y 0 ), and the vector g is a NL-l+1 dimensional real vector.

[0084] S105. Construct a cost function based on the data representation of the nominal system model corresponding to the discrete vehicle lateral and longitudinal error model; and construct an optimization problem of data-driven model predictive control based on the cost function.

[0085] The cost function is:

[0086]

[0087] In the formula, JL (u(k), y(k)) represents the cost function, u(k) represents the actual control input data of system k at the current moment, and y(k) represents the actual output data of system k at the current moment. represents the predictive control input data for j time steps ahead at the current time k, represents the predicted output data for j time steps forward at the current time k, Represents the expected output data j time steps ahead at the current time k; Is and The augmented state of the composition, represents the vector consisting of the last l prediction control inputs within the current k prediction range, represents the vector composed of the last l predicted outputs within the current k prediction range; R>0 is the control input weight matrix, Q>0 is the output weight matrix, P>0 is the terminal cost weight matrix, λ g is the weight factor, is the upper bound of noise; g(k) represents the NL-l+1 dimensional real vector composed of the linear combination coefficients of the column vector of the Hankel matrix k at the current moment.

[0088] Based on the cost function J L (u(k), y(k)), the data-driven model predictive control optimization problem is further constructed as:

[0089]

[0090] In the formula, Represents the cost function J at time k L (u(k), y(k)) is minimized, and the optimal solution is obtained under the constraints. Represents the L+l-dimensional vector composed of the predictive control input data at time k, l time steps backward and L time steps forward. Similarly, It represents the L+l-dimensional vector composed of the predicted output data of l time steps backward and L time steps forward at time k, H L+l (u 0 ) represents the input data u controlled by the prior 0 The generated L+l-order Hankel matrix, H L+l (y 0 ) represents the prior output data y 0 The generated L+l order Hankel matrix, u [k-l,k-1] represents the actual control input data of the system l time steps before time k; y [k-l,k-1] represents the actual control output data of the system l time steps before time k; represents the system input data to be predicted at time k; represents the system output data to be predicted at time k; represents the terminal constraint area; is a bounded set of tight constraints on the predictive control input, is a bounded set of tight constraints on the predicted output.

[0091] S106. Solve the optimization problem at each moment, and apply the first l input data in the obtained optimal control sequence to the autonomous driving vehicle system to complete the control of l time step.

[0092] Initially k = 0, the actual control input data u before k = 0 [-l,-1] (0) and output data y [-l,-1] (0) is used as the initial value to solve the optimization problem in step 105 and obtain the optimal solution and g * (0).

[0093] Selecting the optimal control input data The first item As the control input data for the next l time steps, it is applied to the autonomous driving vehicle system; then, when k = l, the actual control input data u before k = l is used [-l,-1] (k) and output data y [-l,-1] (k) is used as the initial value, and the optimization problem in step 105 is solved when k=1 to obtain the optimal solution. g * (l). Select the optimal control input The first item As the control input data for the next l time steps, it is applied to the autonomous driving system, and so on. Towards terminal area ∈ When As the control input data for the next moment, it ensures that the autonomous driving vehicle system remains stable and robust to external interference and environmental changes.

[0094] Those skilled in the art will appreciate that all or part of the steps in the method for implementing the above embodiments may be completed by instructing related hardware through a program, and the corresponding program may be stored in a computer-readable storage medium.

[0095] It should be noted that although the method operations of the above embodiments are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in this specific order, or that all the operations shown must be performed to achieve the desired results. On the contrary, the steps depicted can be performed in a different order. Additionally or alternatively, some steps can be omitted, multiple steps can be combined into one step, and / or one step can be decomposed into multiple steps.

[0096] Embodiment 2:

[0097] like Figure 3 As shown, this embodiment provides an autonomous driving vehicle trajectory tracking device based on data-driven model predictive control, the device includes a selection module 301, a collection module 302, a representation module 303, a construction module 304 and a control module 305, wherein:

[0098] A selection module 301 is used to discretize the continuous vehicle lateral and longitudinal error model to obtain a discrete vehicle lateral and longitudinal error model; based on the discrete vehicle lateral and longitudinal error model, a control input and a system output are selected;

[0099] The acquisition module 302 is used to acquire multiple input data based on the control input and use them as a set of prior input data; construct a corresponding Hankel matrix based on the prior input data and confirm that the acquired prior input data is continuously excited; apply the prior input data to the discrete vehicle lateral and longitudinal error model, and acquire the corresponding prior output data based on the system output; and construct a corresponding Hankel matrix based on the prior output data;

[0100] A representation module 303 is used to represent the dynamic characteristics of the system using a Hankel matrix constructed from a priori input data and a Hankel matrix constructed from a priori output data based on theoretical knowledge of the behavioral system;

[0101] A construction module 304 is used to construct a cost function based on the data representation of the nominal system model corresponding to the discrete vehicle lateral and longitudinal error model; and to construct an optimization problem of data-driven model predictive control based on the cost function;

[0102] The control module 305 is used to solve the optimization problem every l time step, and apply the first l input data in the obtained optimal control sequence to the automatic driving vehicle system to complete the control of l time step; l is a positive integer greater than or equal to 1.

[0103] The specific implementation of each module in this embodiment can refer to the above-mentioned embodiment 1, which will not be described one by one here; it should be noted that the device provided in this embodiment is only illustrated by the division of the above-mentioned functional modules. In actual applications, the above-mentioned functions can be allocated to different functional modules as needed, that is, the internal structure can be divided into different functional modules to complete all or part of the functions described above.

[0104] Embodiment 3:

[0105] This embodiment provides a terminal device, which can be a computer. Figure 4 As shown, it comprises a processor 402, a memory, an input device 403, a display 404 and a network interface 405 connected via a system bus 401. The processor is used to provide computing and control capabilities. The memory comprises a non-volatile storage medium 406 and an internal memory 407. The non-volatile storage medium 406 stores an operating system, a computer program and a database. The internal memory 407 provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. When the processor 402 executes the computer program stored in the memory, the automatic driving vehicle trajectory tracking method based on data-driven model predictive control of the above-mentioned embodiment 1 is implemented.

[0106] Embodiment 4:

[0107] This embodiment provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the method for autonomous driving vehicle trajectory tracking based on data-driven model predictive control of the above-mentioned embodiment 1 is implemented.

[0108] It should be noted that the computer-readable storage medium of the present embodiment may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0109] In summary, the present invention achieves real-time optimal control of unknown systems by directly utilizing limited offline data and behavioral system theory, solves the problem of traditional trajectory tracking methods relying on accurate system modeling, overcomes the problems of complex parameter identification and difficulty in balancing control accuracy and robustness in traditional model predictive control, and effectively responds to the challenges of uncertainty and complexity in dynamic traffic environments. The present invention achieves driving the unknown system along the desired trajectory while satisfying system constraints, ensures high accuracy and robustness of trajectory tracking, and provides an efficient and reliable control strategy for autonomous vehicles.

[0110] The above is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical solution and inventive concept of the present invention within the scope disclosed by the present invention, which shall fall within the protection scope of the present invention.

Claims

1. A method for tracking trajectory of an autonomous driving vehicle based on data-driven model predictive control, characterized in that: The method comprises: Discretize the continuous vehicle lateral and longitudinal error model to obtain a discrete vehicle lateral and longitudinal error model; select the control input and system output based on the discrete vehicle lateral and longitudinal error model; Based on the control input, multiple input data are collected and used as a set of prior input data; a corresponding Hankel matrix is ​​constructed according to the prior input data and it is confirmed that the collected prior input data is continuously excited; Apply the prior input data to the discrete vehicle lateral and longitudinal error model, collect the corresponding prior output data based on the system output; construct the corresponding Hankel matrix based on the prior output data; Based on the theoretical knowledge of behavioral systems, the dynamic characteristics of the system are represented by the Hankel matrix constructed by the prior input data and the Hankel matrix constructed by the prior output data; Based on the data representation of the nominal system model corresponding to the discrete vehicle lateral and longitudinal error model, a cost function is constructed; based on the cost function, an optimization problem of data-driven model predictive control is constructed; The optimization problem is solved at every l time step, and the first l input data in the obtained optimal control sequence are applied to the autonomous driving vehicle system to complete the control of l time step; l is a positive integer greater than or equal to 1.

2. The automatic driving vehicle trajectory tracking method according to claim 1, characterized in that: The discrete vehicle lateral and longitudinal error model is: in: I is the identity matrix; In the formula, x(k+1) and x(k) represent the states of the system at time k+1 and k respectively, u(k) represents the control input at time k, and y(k) represents the system output at time k; are all coefficient matrices; m is the vehicle mass; I z is the moment of inertia of the vehicle around the z axis in the vehicle coordinate system, l f and l r Respectively front wheelbase and rear wheelbase; C af and C ar are the cornering stiffness of the front and rear wheels respectively; V x is the vehicle longitudinal acceleration; Indicates the angular velocity required for the vehicle body to turn the desired angle; According to the discrete vehicle lateral and longitudinal error model, the front wheel steering angle δ and acceleration a of the automatic driving vehicle are selected as control inputs to select input data; the lateral error y is selected e , lateral error rate Heading error θ e , heading error rate Longitudinal error x e and longitudinal error rate As system output to select the output data.

3. The automatic driving vehicle trajectory tracking method according to claim 2, characterized in that: The Hankel matrix constructed based on the behavioral system theory and the Hankel matrix constructed based on the prior input data and the Hankel matrix constructed based on the prior output data represent the dynamic characteristics of the system, including: Ignore the disturbance term in the discrete vehicle lateral and longitudinal error model According to the theoretical knowledge of behavioral systems, any set of input and output data sequences of the system can be expressed as in, is the input vector consisting of the input data sequence, is the output vector consisting of the output data sequence, The input vector and the output vector The augmented vector composed of is the Hankel matrix H constructed from the prior input data L+n (u 0 ) and the Hankel matrix H constructed from the prior output data L+n (y 0 ), the vector g is an NL-l+1 dimensional real vector, N is the number of prior input data and N ≥ (m+1)(L+l+n-1), n ​​is the state dimension of the discrete vehicle lateral and longitudinal error model, m is the input data dimension, L is the prediction domain of the data-driven model predictive control and L>n+l.

4. The automatic driving vehicle trajectory tracking method according to any one of claims 1 to 3, characterized in that: The step of constructing a corresponding Hankel matrix according to the priori input data and confirming that the collected priori input data is continuously excited includes: The prior input data is formed into a prior input vector; Construct the corresponding Hankel matrix based on the prior input vector; If the Hankel matrix is ​​of full row rank, it is confirmed that the collected priori input data is continuously excited; otherwise, a new set of priori input data is collected and it is determined whether it is continuously excited, until the collected priori input data is continuously excited.

5. The automatic driving vehicle trajectory tracking method according to claim 1, characterized in that: Solving the optimization problem at every l time step, and applying the first l control input data in the obtained optimal control sequence to the automatic driving vehicle system to complete the control of l time step, includes: At the initial time k=0, the optimization problem is solved by taking the l actual control input data and output data before k=0 as initial values ​​to obtain the optimal control input data sequence and the optimal control output data sequence at time 0; Select the first l items in the optimal control input data sequence as the control input data for the next l time steps and apply them to the system; then when k=l, use the l actual control input data and output data before k=l as the initial values ​​to solve the optimization problem, select the first l items from the obtained optimal control input data sequence as the control input data for the next l time steps and apply them to the system; solve the optimization problem in this way, apply the first l input data in the obtained optimal control input data sequence to the system to complete the control of l time steps and apply it to the system to ensure the stability of the system and improve the robustness.

6. The automatic driving vehicle trajectory tracking method according to claim 1, characterized in that: The optimization problem is: In the formula, Represents the cost function J at time k L (u(k), y(k)) is minimized, and the optimal solution is obtained under the constraints. and g * (k); represents the L+l-dimensional vector composed of the predictive control input data at time k, l time steps backward and L time steps forward, It represents the L+l-dimensional vector composed of the predicted output data of l time steps backward and L time steps forward at time k, H L+l (u 0 ) represents the input data u controlled by the prior 0 The generated L+l-order Hankel matrix, H L+l (y 0 ) represents the prior output data y 0 The generated L+l order Hankel matrix, u [k-l,k-1] represents the actual control input data of the system l time steps before time k; y [k-l,k-1] represents the actual control output data of the system l time steps before time k; represents the system input data to be predicted at time k; represents the system output data to be predicted at time k; represents the terminal constraint area; is a bounded set of tight constraints on the predictive control input, is a bounded set of tight constraints on the predicted output.

7. The method for tracking the trajectory of an autonomous driving vehicle according to any one of claims 1 to 3, 5 and 6, characterized in that: The cost function is: In the formula, J L (u(k), y(k)) represents the cost function, u(k) represents the actual control input data of system k at the current moment, and y(k) represents the actual output data of system k at the current moment. represents the predictive control input data for j time steps ahead at the current time k, represents the predicted output data for j time steps forward at the current time k, Represents the expected output data j time steps ahead at the current time k; Is and The augmented state of the composition, represents the vector consisting of the last l prediction control inputs within the current k prediction range, Represents the vector consisting of the last l prediction outputs within the k prediction range at the current moment; is the control input weight matrix, is the output weight matrix, is the weight matrix of the terminal cost, λ g is the weight factor, is the upper bound of the noise; g(k) represents the NL-l+1 dimensional real vector composed of the linear combination coefficients of the column vector of the k Hankel matrix at the current moment.

8. An autonomous driving vehicle trajectory tracking device based on data-driven model predictive control, characterized in that: The device comprises: A module is selected to discretize the continuous vehicle lateral and longitudinal error model to obtain a discrete vehicle lateral and longitudinal error model; based on the discrete vehicle lateral and longitudinal error model, a control input and a system output are selected; The acquisition module is used to acquire multiple input data based on the control input and use them as a set of prior input data; construct a corresponding Hankel matrix based on the prior input data and confirm that the acquired prior input data is continuously excited; apply the prior input data to the discrete vehicle lateral and longitudinal error model, and acquire the corresponding prior output data based on the system output; and construct a corresponding Hankel matrix based on the prior output data; A representation module is used to represent the dynamic characteristics of the system using the Hankel matrix constructed from the prior input data and the Hankel matrix constructed from the prior output data based on the theoretical knowledge of the behavioral system; A construction module is used to construct a cost function based on the data representation of the nominal system model corresponding to the discrete vehicle lateral and longitudinal error model; based on the cost function, an optimization problem of data-driven model predictive control is constructed; A control module is used to solve the optimization problem every l time steps, and apply the first l input data in the obtained optimal control sequence to the automatic driving vehicle system to complete the control of l time steps; l is a positive integer greater than or equal to 1.

9. A terminal device, comprising a processor and a memory for storing a program executable by the processor, characterized in that: When the processor executes the program stored in the memory, the automatic driving vehicle trajectory tracking method described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the automatic driving vehicle trajectory tracking method according to any one of claims 1 to 7 is implemented.

Images