Pipeline-based hybrid vehicle queue robust data driving control method and device

Abstract

The invention relates to a pipeline-based hybrid vehicle queue robust data driving control method and device. Comprising the steps of obtaining a target input data sequence and a target output data sequence based on a to-be-controlled hybrid vehicle queue; creating an over-approximation system matrix set based on the target input and output data sequences, and determining a data-driven minimum robust positive invariant set according to the over-approximation system matrix set; determining a pipeline-based robust data driving prediction control strategy based on the minimum robust positive invariant set, and calculating optimal nominal control input and error feedback control input of the intelligent networked vehicle in combination with a preset rolling time domain control framework; and obtaining a final control input based on the optimal nominal control input and the error feedback control input, so that the controller controls the hybrid vehicle queue to be controlled based on the final control input. Therefore, the problems of low calculation efficiency, insufficient robustness and the like of the existing control strategy are solved, the safety control of the hybrid motorcade is realized, and the driving safety is improved.

Description

Technical Field

[0001] The present application relates to the field of vehicle control technology, and in particular to a pipeline-based hybrid vehicle platoon robust data-driven control method and device. Background Art

[0002] With the rapid development of intelligent and connected vehicles (ICVs) technology, it is expected that the future intelligent transportation system will operate in a mixed traffic environment, which will accommodate the coexistence of ICVs and human-driven vehicles (HDVs) to form mixed fleets. These mixed fleets have great potential to improve traffic stability and efficiency, which in turn promotes the development of advanced control strategies for mixed fleets.

[0003] In related technologies, strategies such as distributed data-driven model predictive control and data-supported predictive control have been proposed, and the control of mixed fleets has been verified in various scenarios such as simulation research, hardware-in-the-loop experiments, and driver-in-the-loop experiments.

[0004] However, most of these methods are verified under idealized conditions, which limits their application in the real world, leading to problems such as low efficiency and lack of robustness, which need to be urgently addressed. Summary of the invention

[0005] The present application provides a pipeline-based mixed vehicle fleet robust data-driven control method and device to solve the problems of low computational efficiency and insufficient robustness of existing control strategies, achieve safe control of mixed vehicle fleets, and improve driving safety.

[0006] To achieve the above-mentioned object, the first embodiment of the present application proposes a pipeline-based hybrid vehicle platoon robust data-driven control method, comprising the following steps:

[0007] Acquire a target input data sequence and a target output data sequence based on the mixed vehicle queue to be controlled;

[0008] Creating an over-approximation system matrix set based on the target input data sequence and the target output data sequence, and determining a data-driven minimum robust positive invariant set according to the over-approximation system matrix set;

[0009] Determining a pipeline-based robust data-driven predictive control strategy integrating safety and performance constraints based on the minimum robust positive invariant set, and calculating an optimal nominal control input and an error feedback control input of the intelligent connected vehicle using a preset receding horizon control framework and the pipeline-based robust data-driven predictive control strategy;

[0010] A final control input of the intelligent connected vehicle is obtained based on the optimal nominal control input and the error feedback control input, so that a controller controls the mixed vehicle queue to be controlled based on the final control input.

[0011] According to one embodiment of the present application, determining a data-driven minimum robust positive invariant set according to the over-approximation system matrix set includes:

[0012] Determine a subsystem matrix set according to the over-approximation system matrix set;

[0013] Based on a preset probability measure, a finite sample set is determined from the subsystem matrix set, a linear matrix inequality is solved using each sample pair in the finite sample set to obtain a first optimization variable and a second optimization variable, and a stable feedback control law is determined according to the first optimization variable and the second optimization variable;

[0014] The stable feedback control law is used to calculate the data-driven minimum robust positive invariant set.

[0015] According to one embodiment of the present application, the pipeline-based robust data-driven predictive control strategy is:

[0016]

[0017] Subject to:

[0018]

[0019] ∈ z (i|k)=∈(k);

[0020] x z (0|k)=x z (k);

[0021]

[0022] Where u(k) is the control input data sequence, x(k) is the state output data sequence, r(i|k) is the desired reference state at time step k+i, Q is the penalty state deviation matrix, R is the control input matrix, and x z (i|k) is the state of the nominal system, u z (i|k) is the control input of the nominal system, ∈ z (i|k) is the disturbance input of the nominal system, x z (0|k) is the initial state of the nominal system, is the reachable set of the (i+1|k)th step of the nominal system, is the reachable set of the (i|k)th step of the nominal system, is the initial reachable set of the nominal system, is the set of over-approximated system matrices, is the state constraint, is the control input constraint, K is the stable feedback control rate, is the minimum robust positive invariant set, k is the time step, i is a natural number, and N is an integer.

[0023] According to one embodiment of the present application, the final control input is:

[0024]

[0025] in, is the first control input, u e (k) is the error feedback control input, K is the stable feedback control law, x e (k) is the state of the error system.

[0026] According to one embodiment of the present application, the step of acquiring a target input data sequence and a target output data sequence based on a hybrid vehicle queue to be controlled includes:

[0027] Applying a preset control input, a preset interference input and a preset noise input to the mixed vehicle queue to be controlled to obtain an initial input data sequence and an initial output data sequence;

[0028] The initial input data sequence and the initial output data sequence are recombined respectively to obtain the target input data sequence and the target output data sequence.

[0029] According to the pipeline-based hybrid vehicle fleet robust data-driven control method proposed in the embodiment of the present application, by obtaining the target input data sequence and the target output data sequence of the hybrid vehicle fleet to be controlled, an over-approximation system matrix set is constructed to determine the minimum robust positive invariant set. On this basis, combined with safety and performance constraints, a pipeline-based robust data-driven predictive control strategy is formulated, and the optimal control input of the intelligent connected vehicle is calculated. Finally, the controller can use these control inputs to manage the hybrid vehicle fleet to be controlled. In this way, the problems of low computational efficiency and insufficient robustness of the existing control strategy are solved, and the safe control of the hybrid vehicle fleet is achieved, which improves driving safety.

[0030] To achieve the above-mentioned purpose, the second embodiment of the present application proposes a pipeline-based hybrid vehicle platoon robust data-driven control device, comprising:

[0031] An acquisition module, used for acquiring a target input data sequence and a target output data sequence based on the mixed vehicle queue to be controlled;

[0032] A determination module, configured to create an over-approximation system matrix set based on the target input data sequence and the target output data sequence, and determine a data-driven minimum robust positive invariant set according to the over-approximation system matrix set;

[0033] A calculation module, used to determine a pipeline-based robust data-driven predictive control strategy integrating safety and performance constraints based on the minimum robust positive invariant set, and calculate the optimal nominal control input and error feedback control input of the intelligent connected vehicle by using a preset rolling horizon control framework and the pipeline-based robust data-driven predictive control strategy;

[0034] A control module is used to obtain a final control input of the intelligent connected vehicle based on the optimal nominal control input and the error feedback control input, so that a controller controls the mixed vehicle queue to be controlled based on the final control input.

[0035] According to an embodiment of the present application, the determining module is specifically used to:

[0036] Determine a subsystem matrix set according to the over-approximation system matrix set;

[0037] Based on a preset probability measure, a finite sample set is determined from the subsystem matrix set, a linear matrix inequality is solved using each sample pair in the finite sample set to obtain a first optimization variable and a second optimization variable, and a stable feedback control law is determined according to the first optimization variable and the second optimization variable;

[0038] The stable feedback control law is used to calculate the data-driven minimum robust positive invariant set.

[0039] According to one embodiment of the present application, the pipeline-based robust data-driven predictive control strategy is:

[0040]

[0041] Subject to:

[0042]

[0043] ∈ z (i|k)=∈(k);

[0044] x z (0|k)=x z (k);

[0045]

[0046] Where u(k) is the control input data sequence, x(k) is the state output data sequence, r(i|k) is the desired reference state at time step k+i, Q is the penalty state deviation matrix, R is the control input matrix, and x z (i|k) is the state of the nominal system, u z (i|k) is the control input of the nominal system, ∈ z (i|k) is the disturbance input of the nominal system, x z (0|k) is the initial state of the nominal system, is the reachable set of the (i+1|k)th step of the nominal system, is the reachable set of the (i|k)th step of the nominal system, is the initial reachable set of the nominal system, is the set of over-approximated system matrices, is the state constraint, is the control input constraint, K is the stable feedback control rate, is the minimum robust positive invariant set, k is the time step, i is a natural number, and N is an integer.

[0047] According to one embodiment of the present application, the final control input is:

[0048]

[0049] in, is the first control input, u e (k) is the error feedback control input, K is the stable feedback control law, x e (k) is the state of the error system.

[0050] According to one embodiment of the present application, the acquisition module is specifically used to:

[0051] Applying a preset control input, a preset interference input and a preset noise input to the mixed vehicle queue to be controlled to obtain an initial input data sequence and an initial output data sequence;

[0052] The initial input data sequence and the initial output data sequence are recombined respectively to obtain the target input data sequence and the target output data sequence.

[0053] According to the pipeline-based hybrid vehicle fleet robust data-driven control device proposed in the embodiment of the present application, by acquiring the target input data sequence and the target output data sequence of the hybrid vehicle fleet to be controlled, an over-approximation system matrix set is constructed to determine the minimum robust positive invariant set. On this basis, combined with safety and performance constraints, a pipeline-based robust data-driven predictive control strategy is formulated, and the optimal control input of the intelligent networked vehicle is calculated. Finally, the controller can use these control inputs to manage the hybrid vehicle fleet to be controlled. In this way, the problems of low computational efficiency and insufficient robustness of the existing control strategy are solved, and the safe control of the hybrid vehicle fleet is achieved, which improves driving safety.

[0054] To achieve the above-mentioned objectives, the third aspect of the present application proposes an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the pipeline-based hybrid vehicle platoon robust data-driven control method as described in the above-mentioned embodiments.

[0055] To achieve the above objectives, the fourth aspect of the present application proposes a computer-readable storage medium on which a computer program is stored, and the program is executed by a processor to implement the pipeline-based hybrid vehicle queue robust data-driven control method as described in the above embodiments.

[0056] To achieve the above objectives, a fifth aspect of the present application proposes a computer program product, which includes a computer program. When the computer program is executed by a processor, it is used to implement the pipeline-based hybrid vehicle queue robust data-driven control method as described in the above embodiments.

[0057] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0059] Figure 1 A flowchart of a pipeline-based hybrid vehicle platoon robust data-driven control method provided according to an embodiment of the present application;

[0060] Figure 2 is a schematic diagram of a mixed vehicle platoon according to an embodiment of the present application;

[0061] Figure 3 is a flowchart of offline data collection for a mixed vehicle queue according to an embodiment of the present application;

[0062] Figure 4is a flowchart of offline learning for a mixed vehicle platoon according to an embodiment of the present application;

[0063] Figure 5 is an online control flow chart for a mixed vehicle queue according to an embodiment of the present application;

[0064] Figure 6 A block diagram of a pipeline-based hybrid vehicle platoon robust data-driven control device provided according to an embodiment of the present application;

[0065] Figure 7 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0066] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0067] The following describes a pipeline-based hybrid vehicle platoon robust data-driven control method and device according to an embodiment of the present application with reference to the accompanying drawings.

[0068] Figure 1 The flowchart of a pipeline-based hybrid vehicle platoon robust data-driven control method according to an embodiment of the present application.

[0069] Before introducing the pipeline-based hybrid vehicle platoon robust data-driven control method proposed in the embodiment of the present application, the relevant technical background is first introduced.

[0070] The presence of HDVs in mixed vehicle platoons introduces a human-in-the-loop system, which is inherently complex due to the unpredictable nature of human driving behavior. Current research mainly relies on model-based approaches to address the associated control challenges. These approaches typically involve modeling the behavior of HDVs using well-established driver behavior models, such as the Optimal Velocity Model (OVM) and the Intelligent Driver Model (IDM). The resulting system dynamics can serve as the basis for designing controllers using techniques such as optimal control, robust control, and model predictive control (MPC). These controllers are designed to generate control inputs for ICVs to stabilize and optimize the behavior of mixed vehicle platoons. However, the effectiveness of these model-based approaches is often limited by the inherent uncertainties of human driving behavior. These uncertainties, coupled with the complexity of vehicle dynamics and chassis components, make it challenging to develop accurate models for mixed vehicle platoons based solely on first principles. Such modeling inaccuracies can degrade the performance of control algorithms, affect the stability of mixed vehicle platoons, exacerbate traffic fluctuations, and even pose safety risks.

[0071] To address the modeling challenges in controlling mixed vehicle platoons, data-driven control methods have shown great potential as an innovative alternative. Compared with traditional model-based methods, which rely on predefined system dynamics, data-driven methods directly use input-output data to predict system behavior. These methods, through input-output data, are able to predict the future output of the system and optimize the control input to achieve specific performance goals, providing a flexible framework for the management of complex systems.

[0072] In the field of mixed vehicle platoon control, researchers have proposed a variety of data-driven strategies, including distributed data-driven model predictive control, data-supported predictive control, and their derivatives. These strategies have shown promising results in a variety of scenarios, including simulation studies, hardware-in-the-loop experiments, and driver-in-the-loop experiments, especially in dealing with traffic waves. Despite these advances, most data-driven methods have been validated under idealized conditions, which limits their application in the real world. In actual operation, the formation and operation of mixed vehicle platoons are significantly affected by the communication network, which is susceptible to noise. In addition, the speed fluctuations of the head vehicle outside the platoon often introduce disturbances that propagate through the system. Current research has not fully addressed how to enhance the resistance to noise and disturbances in data-driven control frameworks. This oversight weakens the reliability and robustness of current methods and may undermine their performance in real-world applications where noise and disturbances are prevalent.

[0073] Recent research advances have focused on improving the robustness of control systems for mixed vehicle platoons. For example, a data-supported predictive control framework enhanced with min-max robust optimization has shown effectiveness in mitigating unknown disturbances from the head vehicle. Another notable approach is the fully symmetric polytope (zonotope) data-driven predictive control, which exploits linear reachability analysis to mitigate the effects of observation noise on data collection. Although these approaches represent significant advances, they also introduce significant limitations. For example, the use of min-max robust optimization involves solving a complex two-level optimization problem, which increases the computational cost and is not suitable for real-time applications. Similarly, the zonotope data-driven predictive control approach requires multi-step forward reachability set computations, which requires significant computational resources and high numerical precision. Therefore, addressing these computational inefficiencies is critical to advancing robust data-driven control strategies in mixed vehicle platoon systems.

[0074] Based on the above problems, an embodiment of the present application proposes a pipeline-based hybrid vehicle queue robust data-driven control method. The method is based on pipeline robust data-driven predictive control (TRDDPC). By collecting input-output data from the hybrid vehicle queue, a matrix zonotope is constructed to directly deal with system uncertainty, and then the data-driven minimum robust positive invariant set is calculated, the boundaries of system uncertainty and interference are clearly defined, and on the basis of the minimum robust positive invariant set, a nominal TRDDPC optimization problem with integrated safety constraints is formulated, and the rolling horizon control framework is used to solve it to obtain the final control input.

[0075] Next, the pipeline-based hybrid vehicle platoon robust data-driven control method proposed in the embodiment of the present application will be described in detail.

[0076] For example, Figure 1 As shown, the pipeline-based hybrid vehicle platoon robust data-driven control method includes the following steps:

[0077] In step S101 , a target input data sequence and a target output data sequence are obtained based on a hybrid vehicle queue to be controlled.

[0078] Among them, Figure 2 As shown, the mixed vehicle queue to be controlled may be a mixed queue consisting of an intelligent connected vehicle (ICV) and multiple human-driven vehicles (HDVs), as well as a tracked vehicle outside the mixed vehicle queue.

[0079] Specifically, specific data can be collected first to provide a basis for subsequent control strategies. In the data collection phase of the embodiment of the present application, the system of the mixed vehicle queue to be controlled can be stimulated by applying a small control input to the intelligent networked vehicle in the mixed vehicle queue to be controlled and a disturbance (i.e., disturbance input) to the head vehicle (i.e., the tracked vehicle), thereby collecting offline data, i.e., a target input data sequence and a target output data sequence.

[0080] The following further describes how to obtain a target input data sequence and a target output data sequence based on the mixed vehicle queue to be controlled.

[0081] As a possible implementation method, in some embodiments, a target input data sequence and a target output data sequence are obtained based on the mixed vehicle queue to be controlled, including: applying a preset control input, a preset interference input and a preset noise input to the mixed vehicle queue to be controlled to obtain an initial input data sequence and an initial output data sequence; and respectively recombine the initial input data sequence and the initial output data sequence to obtain a target input data sequence and a target output data sequence.

[0082] Specifically, the state vector x(k) of the mixed vehicle platoon system to be controlled is mainly affected by the following factors: control input u(k) (i.e., preset control input), head vehicle speed ∈(k) (i.e., preset interference input) and random noise input ω(k) (i.e., preset noise input). In the process of data collection, the three quantities u(k), ∈(k) and ∈(k) are measurable, while the preset noise input ω(k) cannot be directly measured. However, in the embodiment of the present application, it is assumed that ω(k) is finite. Figure 3 As shown, in order to facilitate data collection, the preset control input u(k) and the preset interference input ∈(k) can continuously act on the hybrid vehicle platoon system to be controlled within a certain time (such as T+1 time step). This continuous excitation method helps to collect a series of data (i.e., the initial input data sequence and the initial output data sequence), which can show the performance of the hybrid vehicle platoon system to be controlled under different controls and interferences. Among them, the initial input data sequence includes the control input U and the interference input E, and the initial output data sequence is the corresponding state output X, that is:

[0083] U=[u(1),u(2),…,u(T+1)]∈R 1×(T+1) ;

[0084] E=[∈(1),∈(2),…,∈(T+1)]∈R 1×(T+1) ;

[0085] X=[x(1),x(2),…,x(T+1)]∈R 2n×(T+1) ;

[0086] Furthermore, in order to facilitate offline learning and directly use the above data sequence, the embodiment of the present application recombines the initial input data sequence and the initial output data sequence to obtain the target input data sequence and the target output data sequence as follows:

[0087] U - =[u(1),u(2),…,u(T)]∈R 1×T ;

[0088] E - =[∈(1),∈(2),…,∈(T)]∈R 1×T ;

[0089] X - =[x(1),x(2),…,x(T)]∈R 2n×T ;

[0090] X + =[x(2),x(3),…,x(T+1)]∈R 2n×T ;

[0091] Among them, U - is the reorganized control input, E - is the interference input after reorganization, X - is the system state of the previous step after reorganization, X + It is the system status after the reorganization.

[0092] To facilitate subsequent derivation, the target input data sequence also includes noise input, which can be expressed as:

[0093] W - =[ω(1),ω(2),…,ω(T)]∈R 2n×T ;

[0094] Although ω(k) cannot be measured directly, it is a key component in analyzing the behavior of a mixed vehicle platooning system to be controlled.

[0095] In step S102, an over-approximation system matrix set is created based on the target input data sequence and the target output data sequence, and a data-driven minimum robust positive invariant set is determined according to the over-approximation system matrix set.

[0096] Specifically, after the key target output data sequence and target input data sequence are obtained in the data collection phase, the offline learning phase is entered. In the offline learning phase of the embodiment of the present application, since the system model matrix A (system state matrix), B (control input matrix) and H (disturbance input matrix) in the hybrid vehicle queue to be controlled are unknown, in order to solve this problem, the pre-collected target output data sequence and target input data sequence can be used to calculate an over-approximated system matrix (i.e., the set of over-approximated system matrices), which exists in the form of matrixzonotope, can systematically encapsulate all feasible system matrices consistent with the observed data and take into account the influence of noise. This set can contain the uncertain and unknown dynamic behaviors of the hybrid vehicle platoon system to be controlled. By utilizing the set of over-approximated system matrices, the data-driven minimum robustly positive invariant set (RPI) can be further determined, which can clearly define the robustness boundary of the system in the face of uncertainty and external interference.

[0097] Furthermore, if Figure 4 As shown, the over-approximation system matrix set created based on the target input data sequence and the target output data sequence is All possible system models represented as [ABH] can be over-approximated. This construction process ensures that the resulting over-approximated system matrix set It is consistent with the noise data, thus providing a solid foundation for subsequent analysis and processing. The specific construction process is as follows:

[0098] Given the target input data sequence and target output data sequence U obtained by the hybrid vehicle platoon system to be controlled - 、E - , X - , X + , if the matrix has full row rank, then the set of all possible matrices [ABH] (i.e., the set of over-approximated system matrices ) can be expressed as:

[0099]

[0100] in, is the Moore-Penrose pseudoinverse of the matrix, is the noise matrix band set, which can be expressed as:

[0101]

[0102] in, for The center matrix of for The generator matrix of is the number of generated vectors.

[0103] Using the derived over-approximation system matrix set The over-approximation data-driven system model can be obtained as follows:

[0104]

[0105] in, For an over-approximated data-driven system model, The overapproximated reachable set of system states x(k) representing the system dynamics.

[0106] Next, we explain in detail how to determine the data-driven minimum robust positive invariant set based on the set of over-approximated system matrices.

[0107] As a possible implementation method, in some embodiments, a data-driven minimum robust positive invariant set is determined based on an over-approximation system matrix set, including: determining a subsystem matrix set based on the over-approximation system matrix set; based on a preset probability measure, determining a finite sample set from the subsystem matrix set, using each sample pair in the finite sample set to solve a linear matrix inequality to obtain a first optimization variable and a second optimization variable, and determining a stable feedback control law based on the first optimization variable and the second optimization variable; and using the stable feedback control law to calculate the data-driven minimum robust positive invariant set.

[0108] Specifically, to ensure that the system matrix set is over-approximated To determine the stability of all potential configurations, the embodiment of the present application can first determine the subsystem matrix set It contains all possible realizations of [AB], and its form can be expressed as:

[0109]

[0110] Among them, I is the unit matrix and O is the zero matrix, and the dimensions of both are consistent with the system space.

[0111] In order to calculate the stable feedback control law K∈R 1×2n , the embodiment of the present application may adopt a probability-based method, that is, in a preset probability measure Next, from the subsystem matrix set Get a finite sample set For each sampling pair (A, B) in the finite sample set, solve the following linear matrix inequality (Linear Matrix Inequality, LMI for short):

[0112]

[0113] Among them, P is the first optimization variable and Z is the second optimization variable.

[0114] After obtaining P and Z, the stable feedback control law K is calculated as follows:

[0115] K=ZP -1 .

[0116] Using this representation, a data-driven stable feedback control law K is derived to ensure Captures the stability of all potential system configurations.

[0117] Next, the stable feedback control law can be used to calculate the minimum robust positive invariant set of data drive. That is, the embodiment of the present application firstly converts the over-approximated data drive system model Decoupled into two components: the nominal system and the error system, the dynamic equations of these two systems can be expressed as:

[0118]

[0119] in, is the reachable set of the k+1th step of the nominal system, is the reachable set of the k-th step of the nominal system, u z (k) is the control input of the nominal system, ∈ z (k) is the prediction error, is the reachable set of the error system at the k+1th step, is the reachable set of the k-th step of the error system, u e (k) is the feedback control input of the error system, ∈ e (k) is the predicted value of the leading vehicle speed, which is set as a constant.

[0120] In the embodiment of the present application, the prediction error ∈ z (k) Limited and meets the following conditions:

[0121] |∈ e (k)| ∞ ≤∈ max ;

[0122] Among them, ∈ max ∈ e (k) The upper limit.

[0123] Further, u z (k) and ∈ z (k), and u e (k),∈ e (k) and the original system variables u(k) and ∈(k) are intrinsically linked, namely:

[0124]

[0125] u(k)= z(k)+ e(k);

[0126] ∈(k)= z(k)+ e(k);

[0127] It should be noted that the noise term ω(k) here only affects the dynamics of the error system but not the dynamics of the nominal system.

[0128] Among them, for the defined error system, the embodiment of the present application uses a stable feedback control law K to stabilize the error system dynamics, thereby generating the following closed-loop error system:

[0129]

[0130] Among them, the matrix and The construction is as follows:

[0131]

[0132] In order to ensure the boundedness of the closed-loop error system, the embodiment of the present application derives the minimum robust positive invariant set of the closed-loop error system, which is denoted as Defined as and The minimal set of all reachable states is given by:

[0133]

[0134] The infinite sum is Minkowski addition. This infinite sum can be calculated only if the matrix is nilpotent. In order to effectively calculate this set in the non-nilpotent case, the embodiment of the present application proposes an approximate method to calculate the minimum robust positive invariant set, namely:

[0135]

[0136]

[0137] In step S103, a pipeline-based robust data-driven predictive control strategy integrating safety and performance constraints is determined based on the minimum robust positive invariant set, and the optimal nominal control input and error feedback control input of the intelligent connected vehicle are calculated using a preset rolling horizon control framework and a pipeline-based robust data-driven predictive control strategy.

[0138] Specifically, if Figure 5 As shown, after the data collection stage and the offline learning stage, the online control stage of the mixed vehicle queue to be controlled is entered next. In this stage, the embodiment of the present application adopts the TRDDPC framework, that is, the time recursive dynamic programming control framework, combined with the preset rolling horizon control framework, to find the optimal solution by stepwise backward calculation, so as to calculate the optimal nominal control input of the intelligent networked vehicle.

[0139] The TRDDPC framework integrates two basic components: the nominal controller and the error controller. Among them, the error controller plays a vital role in compensating for disturbances and noise, ensuring that the system state is maintained within the boundaries set by the minimum robust positive invariant set. The design of the error controller takes into account the inevitable disturbances and noise in actual operation, and dynamically adjusts the control input to maintain the stability and robustness of the system state. At the same time, the nominal controller is used to control the ideal dynamics of the system, which assumes that there is no disturbance or uncertainty and operates based on the nominal reachable set model. This model encapsulates the optimal trajectory of the system in the absence of disturbances and provides an ideal operation blueprint for the system. The main goal of the nominal controller is to confine the system state to a constrained "pipeline", effectively limiting the deviation between the actual operation trajectory and the desired nominal trajectory. By maintaining the nominal state in this tube, the framework ensures that the deviation remains within an acceptable range, thereby maintaining the stability and performance of the system in the face of uncertainties in actual operation. The specific formulation of the TRDDPC optimization problem (i.e., pipeline-based robust data-driven predictive control strategy) is as follows:

[0140]

[0141] Where u(k) is the control input data sequence, x(k) is the state output data sequence, r(i|k) is the desired reference state at time step k+i, Q is the penalty state deviation matrix, R is the control input matrix, and x z (i|k) is the state of the nominal system, u z (i|k) is the control input of the nominal system, k is the time step, i is a natural number, and N is an integer.

[0142] Subject to:

[0143]

[0144] ∈ z (i|k)=∈(k);

[0145] x z (0|k)=x z (k);

[0146]

[0147] Among them, ∈ z (i|k) is the disturbance input of the nominal system, x z (0|k) is the initial state of the nominal system, is the reachable set of the (i+1|k)th step of the nominal system, is the reachable set of the (i|k)th step of the nominal system, is the initial reachable set of the nominal system, is the set of over-approximated system matrices, is the state constraint, is the control input constraint, K is the stable feedback control rate, is the minimum robust positive invariant set.

[0148] Control input data sequence u(k) = {u z (k),u z (k+1),…,u z (k+N-1)}, the state output data sequence x(k)={x z (k),x z (k+1),…,x z (k+N-1)}, and optimize on prediction layer N.

[0149] Furthermore, the desired reference state at time step k+i is:

[0150]

[0151]

[0152] Among them, r i (k) is the target reference state, is the expected vehicle spacing, is the expected vehicle speed.

[0153] State Constraints Defined as:

[0154]

[0155] in, Limit the deviation of spacing and speed. Here, 1 n is a dimensional vector consisting of 1s, represents the Kronecker product.

[0156] Control Input Constraints is defined as:

[0157]

[0158] Among them, umax is the maximum input allowed.

[0159] By solving the optimization problem, the optimal nominal control input of the system is obtained:

[0160]

[0161] And the state sequence:

[0162]

[0163] In step S104, a final control input of the intelligent connected vehicle is obtained based on the optimal nominal control input and the error feedback control input, so that the controller controls the mixed vehicle queue to be controlled based on the final control input.

[0164] Specifically, by setting the first control input in the system's optimal nominal control input With error feedback control input u e (k) are combined to form the final control input for intelligent connected vehicles:

[0165]

[0166] in, is the first control input, u e (k) is the error feedback control input, K is the stable feedback control law, x e (k) is the state of the error system.

[0167] This final control input u(k) ensures robust trajectory tracking and mitigates the adverse effects of noise and disturbances in mixed vehicle platoons.

[0168] In summary: (1) The embodiment of the present application discloses a pipeline-based robust data-driven control technology architecture for mixed vehicle queues. In view of the characteristics of mixed vehicle queue systems, the method explicitly considers the influence of noise and external interference. By adopting a data-driven method to calculate the robust positive invariant set, the control input constraints and safety constraints of the nominal data-driven predictive control problem can be tightened, thereby ensuring the safety and efficient operation of the system under various uncertainties. This solves the problem of insufficient robustness in the existing mixed queue controller design and provides greater possibilities for the efficient operation of the entire traffic system. In addition, it also solves the problem that the existing methods for mixed queue control cannot guarantee safety constraints, realizes safe control of mixed queues, and significantly improves driving safety.

[0169] (2) By directly using a data-driven approach to calculate the robust positive invariant set, the heavy system identification and modeling work in the traditional method is bypassed, the problem that the existing method heavily relies on the model to calculate the robust positive invariant set is solved, and the calculation accuracy of the robust positive invariant set can be guaranteed. This is crucial to improving the control performance of mixed platoons and also provides strong technical support for building intelligent transportation systems.

[0170] (3) Unlike traditional model-based pipeline model predictive control methods that rely on explicit system models, the framework of the embodiments of the present application can learn the pipeline directly from data, eliminating the need for explicit system modeling. This data-driven approach not only simplifies the implementation process, but also enhances adaptability to real-world scenarios.

[0171] (4) Unlike traditional methods, which usually assume that noise and interference are negligible, the framework of the embodiment of the present application explicitly takes these uncertainties into account as zonotopes when constructing the minimum RPI set. This systematic consideration significantly enhances the robustness of the framework. Based on the derived minimum RPI set, the embodiment of the present application formulates the TRDDPC optimization problem to ensure strict compliance with safety constraints and achieve robust and optimal control of autonomous driving vehicles in a mixed vehicle queue. Finally, the control performance of the mixed queue is analyzed through performance indicators, and the energy consumption and speed volatility of the mixed vehicle queue during driving are quantified, thereby providing a strong supporting technology for achieving stable, safe and efficient control of the mixed vehicle queue.

[0172] According to the pipeline-based hybrid vehicle fleet robust data-driven control method proposed in the embodiment of the present application, by obtaining the target input data sequence and the target output data sequence of the hybrid vehicle fleet to be controlled, an over-approximation system matrix set is constructed to determine the minimum robust positive invariant set. On this basis, combined with safety and performance constraints, a pipeline-based robust data-driven predictive control strategy is formulated, and the optimal control input of the intelligent connected vehicle is calculated. Finally, the controller can use these control inputs to manage the hybrid vehicle fleet to be controlled. In this way, the problems of low computational efficiency and insufficient robustness of the existing control strategy are solved, and the safe control of the hybrid vehicle fleet is achieved, which improves driving safety.

[0173] Next, a pipeline-based hybrid vehicle platoon robust data-driven control device proposed according to an embodiment of the present application is described with reference to the accompanying drawings.

[0174] Figure 6 It is a block diagram of a pipeline-based hybrid vehicle platoon robust data-driven control device according to an embodiment of the present application.

[0175] like Figure 6As shown, the pipeline-based hybrid vehicle platoon robust data-driven control device 10 includes: an acquisition module 100 , a determination module 200 , a calculation module 300 and a control module 400 .

[0176] The acquisition module 100 is used to acquire a target input data sequence and a target output data sequence based on the mixed vehicle queue to be controlled;

[0177] A determination module 200, configured to create an over-approximation system matrix set based on a target input data sequence and a target output data sequence, and determine a data-driven minimum robust positive invariant set according to the over-approximation system matrix set;

[0178] A calculation module 300 is used to determine a pipeline-based robust data-driven predictive control strategy integrating safety and performance constraints based on a minimum robust positive invariant set, and calculate an optimal nominal control input and an error feedback control input of an intelligent connected vehicle using a preset rolling horizon control framework and a pipeline-based robust data-driven predictive control strategy;

[0179] The control module 400 is used to obtain a final control input of the intelligent connected vehicle based on the optimal nominal control input and the error feedback control input, so that the controller controls the mixed vehicle queue to be controlled based on the final control input.

[0180] Further, in some embodiments, the determination module 200 is specifically configured to:

[0181] Determine a subsystem matrix set according to the over-approximated system matrix set;

[0182] Based on a preset probability measure, a finite sample set is determined from the subsystem matrix set, a linear matrix inequality is solved using each sample pair in the finite sample set to obtain a first optimization variable and a second optimization variable, and a stable feedback control law is determined according to the first optimization variable and the second optimization variable;

[0183] The data-driven minimum robust positive invariant set is calculated using the stable feedback control law.

[0184] Further, in some embodiments, the pipeline-based robust data-driven predictive control strategy is:

[0185]

[0186] Subject to:

[0187]

[0188] ∈ z (i|k)=∈(k);

[0189] x z (0|k)=xz (k);

[0190]

[0191] Where u(k) is the control input data sequence, x(k) is the state output data sequence, r(i|k) is the desired reference state at time step k+i, Q is the penalty state deviation matrix, R is the control input matrix, and x z (i|k) is the state of the nominal system, u z (i|k) is the control input of the nominal system, ∈ z (i|k) is the disturbance input of the nominal system, x z (0|k) is the initial state of the nominal system, is the reachable set of the (i+1|k)th step of the nominal system, is the reachable set of the (i|k)th step of the nominal system, is the initial reachable set of the nominal system, is the set of over-approximated system matrices, is the state constraint, is the control input constraint, K is the stable feedback control rate, is the minimum robust positive invariant set, k is the time step, i is a natural number, and N is an integer.

[0192] Furthermore, in some embodiments, the final control input is:

[0193]

[0194] in, is the first control input, u e (k) is the error feedback control input, K is the stable feedback control law, x e (k) is the state of the error system.

[0195] Furthermore, in some embodiments, the acquisition module 100 is specifically configured to:

[0196] Applying a preset control input, a preset interference input and a preset noise input to the mixed vehicle queue to be controlled to obtain an initial input data sequence and an initial output data sequence;

[0197] The initial input data sequence and the initial output data sequence are recombined to obtain a target input data sequence and a target output data sequence.

[0198] It should be noted that the above explanation of the embodiment of the pipeline-based hybrid vehicle platoon robust data-driven control method is also applicable to the pipeline-based hybrid vehicle platoon robust data-driven control device of this embodiment, which will not be repeated here.

[0199] According to the pipeline-based hybrid vehicle fleet robust data-driven control device proposed in the embodiment of the present application, by acquiring the target input data sequence and the target output data sequence of the hybrid vehicle fleet to be controlled, an over-approximation system matrix set is constructed to determine the minimum robust positive invariant set. On this basis, combined with safety and performance constraints, a pipeline-based robust data-driven predictive control strategy is formulated, and the optimal control input of the intelligent networked vehicle is calculated. Finally, the controller can use these control inputs to manage the hybrid vehicle fleet to be controlled. In this way, the problems of low computational efficiency and insufficient robustness of the existing control strategy are solved, and the safe control of the hybrid vehicle fleet is achieved, which improves driving safety.

[0200] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0201] A memory 701 , a processor 702 , and a computer program stored in the memory 701 and executable on the processor 702 .

[0202] When the processor 702 executes the program, the pipeline-based hybrid vehicle platoon robust data-driven control method provided in the above embodiment is implemented.

[0203] Furthermore, the electronic device further comprises:

[0204] The communication interface 703 is used for communication between the memory 701 and the processor 702 .

[0205] The memory 701 is used to store computer programs that can be executed on the processor 702 .

[0206] The memory 701 may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.

[0207] If the memory 701, the processor 702 and the communication interface 703 are implemented independently, the communication interface 703, the memory 701 and the processor 702 can be connected to each other through a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0208] Optionally, in a specific implementation, if the memory 701, the processor 702 and the communication interface 703 are integrated on a chip, the memory 701, the processor 702 and the communication interface 703 can communicate with each other through an internal interface.

[0209] The processor 702 may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0210] An embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above pipeline-based hybrid vehicle queue robust data-driven control method.

[0211] An embodiment of the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the pipeline-based hybrid vehicle queue robust data-driven control method as described above is implemented.

[0212] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0213] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0214] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A pipeline-based hybrid vehicle platoon robust data-driven control method, characterized in that: The following steps are involved: Acquire a target input data sequence and a target output data sequence based on the mixed vehicle queue to be controlled; Creating an over-approximation system matrix set based on the target input data sequence and the target output data sequence, and determining a data-driven minimum robust positive invariant set according to the over-approximation system matrix set; Determining a pipeline-based robust data-driven predictive control strategy integrating safety and performance constraints based on the minimum robust positive invariant set, and calculating the optimal nominal control input and error feedback control input of the intelligent connected vehicle using a preset receding horizon control framework and the pipeline-based robust data-driven predictive control strategy; A final control input of the intelligent connected vehicle is obtained based on the optimal nominal control input and the error feedback control input, so that a controller controls the mixed vehicle queue to be controlled based on the final control input.

2. The method according to claim 1, characterized in that: The step of determining a data-driven minimum robust positive invariant set according to the over-approximation system matrix set comprises: Determine a subsystem matrix set according to the over-approximation system matrix set; Based on a preset probability measure, a finite sample set is determined from the subsystem matrix set, a linear matrix inequality is solved using each sample pair in the finite sample set to obtain a first optimization variable and a second optimization variable, and a stable feedback control law is determined according to the first optimization variable and the second optimization variable; The stable feedback control law is used to calculate the data-driven minimum robust positive invariant set.

3. The method according to claim 1, characterized in that The pipeline-based robust data-driven predictive control strategy is: Subject to: ∈ z (i|k)=∈(k); x z (0|k)=x z (k); Where u(k) is the control input data sequence, x(k) is the state output data sequence, r(i|k) is the desired reference state at time step k+i, Q is the penalty state deviation matrix, R is the control input matrix, and x z (i|k) is the state of the nominal system, u z (i|k) is the control input of the nominal system, ∈ z (i|k) is the disturbance input of the nominal system, x z (0|k) is the initial state of the nominal system, is the reachable set of the (I+1|k)th step of the nominal system, is the reachable set of the (i|k)th step of the nominal system, is the initial reachable set of the nominal system, is the set of over-approximated system matrices, is the state constraint, is the control input constraint, K is the stable feedback control rate, is the minimum robust positive invariant set, k is the time step, i is a natural number, and N is an integer.

4. The method according to claim 1, characterized in that The final control input is: in, is the first control input, u e (k) is the error feedback control input, K is the stable feedback control law, x e (k) is the state of the error system.

5. The method according to claim 1, characterized in that The step of acquiring a target input data sequence and a target output data sequence based on the mixed vehicle queue to be controlled includes: Applying a preset control input, a preset interference input and a preset noise input to the mixed vehicle queue to be controlled to obtain an initial input data sequence and an initial output data sequence; The initial input data sequence and the initial output data sequence are recombined respectively to obtain the target input data sequence and the target output data sequence.

6. A pipeline-based hybrid vehicle platoon robust data-driven control device, characterized in that: include: An acquisition module, used for acquiring a target input data sequence and a target output data sequence based on the mixed vehicle queue to be controlled; A determination module, configured to create an over-approximation system matrix set based on the target input data sequence and the target output data sequence, and determine a data-driven minimum robust positive invariant set according to the over-approximation system matrix set; A calculation module, used to determine a pipeline-based robust data-driven predictive control strategy integrating safety and performance constraints based on the minimum robust positive invariant set, and calculate the optimal nominal control input and error feedback control input of the intelligent connected vehicle by using a preset rolling horizon control framework and the pipeline-based robust data-driven predictive control strategy; A control module is used to obtain a final control input of the intelligent connected vehicle based on the optimal nominal control input and the error feedback control input, so that a controller controls the mixed vehicle queue to be controlled based on the final control input.

7. The device according to claim 6, characterized in that The determination module is specifically used for: Determine a subsystem matrix set according to the over-approximation system matrix set; Based on a preset probability measure, a finite sample set is determined from the subsystem matrix set, a linear matrix inequality is solved using each sample pair in the finite sample set to obtain a first optimization variable and a second optimization variable, and a stable feedback control law is determined according to the first optimization variable and the second optimization variable; The stable feedback control law is used to calculate the data-driven minimum robust positive invariant set.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the pipeline-based hybrid vehicle platoon robust data-driven control method as described in any one of claims 1-5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the pipeline-based hybrid vehicle platoon robust data-driven control method as described in any one of claims 1-5.

10. A computer program product, characterized in that It comprises a computer program, which, when executed by a processor, is used to implement the pipeline-based hybrid vehicle platoon robust data-driven control as described in any one of claims 1-5.

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