Multi-vehicle-queue cooperative control method based on distributed model predictive control and medium

Abstract

The invention discloses a multi-vehicle-queue cooperative control method based on distributed model predictive control, and the method comprises the following steps: constructing a multi-vehicle-queue system and a dynamic model, building a one-way communication topological structure, defining the coupling constraint of vehicles and queues based on the communication topological structure, and carrying out the prediction of the multi-vehicle-queue cooperative control. Constructing a distributed model predictive control optimization problem and a cost function, wherein the distributed model predictive control optimization problem comprises a vehicle node local optimization problem, a queue global optimization problem and a multi-target optimization problem; and solving an optimization control problem for each vehicle node, and adjusting the control input and the motion state of each vehicle. According to the technical scheme, dynamic synchronization and stable control between vehicles and between queues are achieved by means of one-way communication topology design, introduction of coupling constraints inside the queues and between the queues, construction of a local optimization problem and combination of a global coordination mechanism, and the cooperative control performance of a multi-vehicle queue system in a complex dynamic environment can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle platoon cooperative control, and in particular to a multi-vehicle platoon cooperative control method and medium based on distributed model predictive control. Background Art

[0002] With the rapid development of intelligent transportation systems and modern logistics, the coordinated control of large-scale vehicle platoons has become an important research direction in the field of autonomous driving. In a one-way communication environment, there are delays and dynamic changes in information transmission between vehicles. Existing control methods are difficult to achieve efficient coordination and global stability in multi-queue systems. Existing centralized control methods are difficult to meet the needs of real-time coordination of large-scale fleets due to their high computational complexity, large communication delays, and single-point failure risks. Distributed model predictive control (DMPC) has become an effective method to solve this problem due to its combination of local optimization and global coordination. However, current research still has shortcomings in communication topology design, computational complexity control, and dynamic environment adaptability. Therefore, for the problem of multi-queue vehicle trajectory synchronization in a one-way communication environment, it is necessary to propose an efficient, stable, and dynamic environment-adaptive control method to achieve global consistency and robustness of the platoon system. Summary of the invention

[0003] The main purpose of the present invention is to provide a multi-vehicle platoon cooperative control method based on distributed model predictive control, aiming to solve the problems that the existing distributed model predictive control methods still have deficiencies in communication topology design, computational complexity control and dynamic environment adaptability in the cooperative control application of vehicle platoons.

[0004] To achieve the above object, the present invention proposes a multi-vehicle platoon cooperative control method based on distributed model predictive control, comprising the following steps: Constructing a multi-vehicle platoon system and a dynamic model, wherein the dynamic model includes a system state vector, a motion state equation of a single vehicle, and a platoon length state equation; Establishing a unidirectional communication topology structure, wherein the communication topology structure includes an internal queue communication topology and an inter-queue communication topology, wherein the internal queue communication topology includes a directed spanning tree with a pilot vehicle as a root node, and the inter-queue communication topology includes a directed spanning tree with a pilot vehicle of a first queue of the system as a root node; Based on the communication topology, coupling constraints between vehicles and queues are defined, and a distributed model predictive control optimization problem and a cost function are constructed. The distributed model predictive control optimization problem includes a local optimization problem for vehicle nodes, a global optimization problem for queues, and a multi-objective optimization problem integrating the local optimization problem for vehicle nodes and the global optimization problem for queues. The local optimization problem for vehicle nodes is used to solve the local optimal control input for each vehicle node, and the global optimization problem for queues is used to solve the coordinated control input between queues for each queue node using the multi-objective optimization problem. The optimization control problem is solved for each vehicle node, and the control input and motion state of each vehicle are adjusted according to the optimization results to achieve coordinated control within and between queues.

[0005] Preferably, the step of constructing a multi-vehicle platoon system and a dynamic model, wherein the dynamic model includes a system state vector, a motion state equation of a single vehicle, and a platoon length state equation, comprises: defining the update rules of the vehicle state and vehicle state variables, wherein the vehicle state vector includes position, velocity, acceleration and torque, and constructing the dynamic equations of a single vehicle using a discrete time model; The queue length is defined as the distance between the leading car and the tail car, and an equation for the dynamic change of the queue length is established.

[0006] Preferably, the vehicle node local optimization problem includes a vehicle local cost function and vehicle constraints, and the vehicle constraints include a vehicle prediction model constraint, an initial predicted state equal to the actual state constraint at time t, a vehicle state constraint, a vehicle control input constraint, a vehicle position constraint, and recursive feasibility and stability constraints.

[0007] Preferably, the vehicle local cost function includes: ; Where: and They represent the predicted state and predicted control input sequence of vehicle node i at time t, represents the assumed state of vehicle node i at time t; represents the hypothetical state of the preceding vehicle at vehicle node i at time t, Represents the distance between vehicle node i and the preceding vehicle; weight matrix are set to be symmetric non-negative definite matrices, representing the control state error, control input, assumed state error, and leading vehicle distance error, respectively.

[0008] Preferably, the queue global optimization problem includes a queue global cost function and queue constraints, wherein the queue constraints include communication topology constraints, queue position constraints and queue length change constraints, wherein the communication topology constraints are used to constrain the state update of the leader vehicle to depend on the state information of other leader vehicles defined by the communication topology, thereby ensuring that the behavior of the leader vehicle is restricted by the communication graph structure and can only obtain information from neighbors in the communication topology, and the queue position constraints are used to set the position trajectories of the leader vehicle and the tail vehicle in the queue to ensure that the space constraints between queues are met.

[0009] Preferably, the queue global cost function includes: ; Where: represents the predicted state sequence of the pilot vehicle; represents the predictive control input sequence of the pilot vehicle, represents the k-1 queue length; represents the distance error between adjacent pilot vehicles in the queue, represents the distance between the leading vehicle in queue k and the last vehicle in the previous queue; It is set as a symmetric non-negative definite matrix, which controls the state error, control input, assumed state error and leading vehicle distance error respectively to ensure the stability and coordination of vehicles in the queue.

[0010] Preferably, the step of adjusting the motion state of each vehicle according to the optimization result, the motion state including position, speed and acceleration, to achieve coordinated control within and between queues includes: Vehicle status, input status and queue length initialization; For each platoon, the expected trajectory of the leader vehicle is generated in the prediction time domain; At each moment, the optimization control problem is solved for each vehicle node, and based on the optimization results, the optimal control input sequence is obtained to achieve coordinated control within and between queues.

[0011] Preferably, the steps of solving the optimization control problem for each vehicle node at each moment to obtain the optimal control input sequence to achieve the coordinated control within and between queues include: The queue status update step, node status update step, and information exchange and synchronization step are continuously circulated at each moment, gradually updating the control input and status of all queues and vehicle nodes to achieve coordinated control within and between queues; The queue state updating step includes: updating the state sequence of the pilot vehicle to ensure that the desired trajectory is followed; updating the state of each vehicle according to the optimal control input sequence within the prediction time domain, and calculating the next step hypothetical input sequence of the pilot vehicle and updating the hypothetical state sequence; The node state updating step includes: updating the current node state using the optimal control input, calculating the optimal state sequence and updating the node state in the prediction time domain, updating the assumed input sequence of the vehicle, and predicting the assumed state sequence based on the assumed input sequence; The information exchange and synchronization steps include: sending the assumed state sequence and assumed input sequence of the queue leader vehicle node to the adjacent leader vehicle node, and sending the node assumed state sequence and assumed input sequence to the adjacent follower vehicle node to achieve information sharing.

[0012] Preferably, after the step of establishing a unidirectional communication topology structure, wherein the communication topology structure includes an internal queue communication topology and an inter-queue communication topology, wherein the internal queue communication topology includes a directed spanning tree with the pilot vehicle as the root node, and the inter-queue communication topology includes a directed spanning tree with the pilot vehicle of the first queue of the system as the root node, the following steps are included: Establish multiple communication strategies, including a leader-to-leader strategy, a leader-to-last-car strategy, and a leader-to-last-car strategy; the leader-to-leader strategy includes a communication method in which information is transmitted step by step through the leader car of the queue, the leader-to-last-car strategy includes a communication method in which the leader car directly receives information from the last car of the previous queue, and the leader-to-last-car strategy includes a communication method in which the leader car is allowed to simultaneously receive information from the leader car and the last car of the previous queue; Select different communication strategies based on queue length, response speed, and dynamic environment.

[0013] A multi-vehicle platoon cooperative control medium based on distributed model predictive control stores executable instructions, wherein the executable instructions include program codes for executing the multi-vehicle platoon cooperative control method based on distributed model predictive control as described above when executed.

[0014] In the technical solution of the present invention, the unidirectional communication topology design: establishes communication structures within and between queues, adopts a directed tree structure with a pilot vehicle as the root node within the queue, and adopts a directed graph structure with a pilot vehicle in the first queue of the system as the root node between queues, so as to achieve low-latency and efficient information transmission, and the distributed model predictive control method: by introducing coupling constraints within and between queues, constructing local optimization problems, and combining with a global coordination mechanism, dynamic synchronization and stable control between vehicles and queues are achieved, which can effectively improve the collaborative control performance of the multi-vehicle queue system in a complex dynamic environment, and achieve efficient, stable and safe vehicle formation operation, with great improvements in communication topology design, computational complexity control and dynamic environment adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] 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.

[0016] Figure 1 A topological structure diagram of the communication between pilot vehicles in the multi-vehicle platoon cooperative control method based on distributed model predictive control of the present invention; Figure 2 It is a speed variation diagram under the pilot vehicle-pilot vehicle communication structure in the multi-vehicle platoon cooperative control method based on distributed model predictive control of the present invention (the three platoons of red, green and blue, the dotted line is the pilot vehicle); Figure 3 It is a displacement change diagram under the pilot vehicle-pilot vehicle communication structure in the multi-vehicle platoon cooperative control method based on distributed model predictive control of the present invention (red, green and blue three platoons, the dotted line is the pilot vehicle); Figure 4 It is a torque variation diagram under the pilot vehicle-pilot vehicle communication structure in the multi-vehicle platoon cooperative control method based on distributed model predictive control of the present invention (red, green and blue three fleets, the dotted line is the pilot vehicle); Figure 5 This is a diagram of acceleration changes under the pilot vehicle-pilot vehicle communication structure in the multi-vehicle platoon cooperative control method based on distributed model predictive control of the present invention (the three platoons of red, green and blue, the dotted line is the pilot vehicle); Figure 6 It is a communication topology diagram of the leading vehicle and the trailing vehicle in the multi-vehicle platoon cooperative control method based on distributed model predictive control of the present invention; Figure 7 It is a speed variation diagram under the communication structure of the pilot vehicle and the tail vehicle in the multi-vehicle platoon cooperative control method based on distributed model predictive control of the present invention (the three platoons of red, green and blue, the dotted line is the pilot vehicle); Figure 8 It is a displacement change diagram under the communication structure of the pilot vehicle and the tail vehicle in the multi-vehicle platoon cooperative control method based on distributed model predictive control of the present invention (the three platoons of red, green and blue, the dotted line is the pilot vehicle); Fig. 9 It is a torque variation diagram under the communication structure of the pilot vehicle and the trailing vehicle in the multi-vehicle platoon cooperative control method based on distributed model predictive control of the present invention (the three platoons of red, green and blue, the dotted line is the pilot vehicle); Fig.10 This is a diagram of acceleration changes under the communication structure of the lead vehicle and the rear vehicle in the multi-vehicle platoon cooperative control method based on distributed model predictive control of the present invention (the three platoons of red, green and blue, the dotted line is the lead vehicle); Fig.11 It is a communication topology diagram of the pilot vehicle, the pilot vehicle and the trailing vehicle in the multi-vehicle platoon cooperative control method based on distributed model predictive control of the present invention; Fig.12 It is a speed variation diagram under the communication structure of the pilot vehicle, the pilot vehicle and the tail vehicle in the multi-vehicle platoon cooperative control method based on distributed model predictive control of the present invention (the three platoons of red, green and blue, the dotted line is the pilot vehicle); Fig.13 It is a displacement change diagram under the communication structure of the pilot vehicle, the pilot vehicle and the tail vehicle in the multi-vehicle platoon cooperative control method based on distributed model predictive control of the present invention (the three platoons of red, green and blue, the dotted line is the pilot vehicle); Fig.14 It is a torque variation diagram under the communication structure of the pilot vehicle, the pilot vehicle and the tail vehicle in the multi-vehicle platoon cooperative control method based on distributed model predictive control of the present invention (the three platoons of red, green and blue, the dotted line is the pilot vehicle); Fig.15 It is a diagram of acceleration changes under the communication structure of the pilot vehicle, the pilot vehicle and the tail vehicle in the multi-vehicle platoon cooperative control method based on distributed model predictive control of the present invention (the three platoons of red, green and blue, the dotted line is the pilot vehicle); Fig.16 It is the weight matrix value of the DMPC controller in one embodiment of the multi-vehicle platoon cooperative control method based on distributed model predictive control of the present invention.

[0017] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of 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.

[0019] In addition, in the present invention, descriptions such as "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0020] In the present invention, unless otherwise clearly specified and limited, the terms "connection", "fixation", etc. should be understood in a broad sense. For example, "fixation" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0021] In addition, the technical solutions between the various embodiments of the present invention can be combined with each other, but it must be based on the fact that ordinary technicians in the field can implement it. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0022] Please refer to Figure 1-Figure 16 The present invention proposes a multi-vehicle platoon cooperative control method based on distributed model predictive control, comprising the following steps: S100, constructing a multi-vehicle platoon system and a dynamic model, wherein the dynamic model includes a system state vector, a motion state equation of a single vehicle, and a platoon length state equation; S200, establishing a unidirectional communication topology structure, wherein the communication topology structure includes an internal queue communication topology and an inter-queue communication topology, wherein the internal queue communication topology includes a directed spanning tree with a pilot vehicle as a root node, and the inter-queue communication topology includes a directed spanning tree with a pilot vehicle of a first queue of the system as a root node; S300, based on the communication topology, define the coupling constraints of the vehicle and the queue, and construct a distributed model predictive control optimization problem, wherein the distributed model predictive control optimization problem includes a vehicle node local optimization problem, a queue global optimization problem, and a multi-objective optimization problem integrating the vehicle node local optimization problem and the queue global optimization problem; the vehicle node local optimization problem is used to solve the local optimal control input for each vehicle node, and the queue global optimization problem is used to solve the coordinated control input between queues for each queue node using the multi-objective optimization problem; S400, solving an optimization control problem for each vehicle node, and adjusting the control input and motion state of each vehicle according to the optimization result to achieve coordinated control within and between queues.

[0023] In the technical solution of the present invention, the unidirectional communication topology design: establishes communication structures within and between queues, adopts a directed tree structure with the pilot vehicle as the root node within the queue, and adopts a directed graph structure with the pilot vehicle of the first queue of the system as the root node between queues, so as to achieve low-latency and efficient information transmission, and the distributed model predictive control method: by introducing coupling constraints within and between queues, constructing local optimization problems, and combining with the global coordination mechanism, dynamic synchronization and stable control between vehicles and queues are achieved.

[0024] In another embodiment of the present invention, the step of S100 includes: S110, defining a vehicle state and an update rule for a vehicle state variable, wherein the vehicle state vector includes position, velocity, acceleration and torque, and constructing a dynamic equation of a single vehicle using a discrete time model; S120. Define the queue length as the distance between the leading vehicle and the rear vehicle, and establish a dynamic change equation for the queue length.

[0025] Specifically, define a multi-vehicle platoon system Consider a A multi-queue system consisting of a vehicle queue (including a pilot queue LP and M follower queues FP), with queues numbered from 0 to M, where number 0 is the pilot queue of the queue system, and the remaining follower queues are numbered Each queue k Include Vehicles (including a pilot car LV and Following vehicles FV), vehicle numbers range from 0 to , where number 0 is the leader car, number To follow the vehicle, Figure 1 As shown. Define the queue k The vehicle set is: The set of all vehicles in the system is , which can be expressed as: ; Constructing a dynamic model of a multi-queue system: The state vector of the system is defined as .

[0026] in , , and Respectively represent k The first i Car at time t Position, velocity, acceleration and driving force or braking torque.

[0027] The present invention assumes is a discrete time interval, then k The first in the queue i The discrete-time model of a vehicle can be expressed as: ; in: , , , Respectively represent k The first i The total force on the vehicle per unit discrete time, vehicle mass, control input, and inertia lag of longitudinal dynamics.

[0028] Therefore k The first i The motion state of the vehicle can be expressed as: ; ; .

[0029] in, , Respectively represent k The first i The weight matrix of the vehicle's state and control input, Indicates tire radius, is the mechanical efficiency of the transmission system.

[0030] The queue system is composed of multiple queues. The relative positions, speeds and dynamic behaviors of the queues will affect each other. To describe the length or density of the queue, define is the distance between the leading vehicle and the rear vehicle, that is ,in, , Respectively represent k The positions of the lead car and the tail car in a queue.

[0031] The change in queue length is determined by the speed difference between the leading vehicle and the rear vehicle. ; Therefore, the change in the queue length state can be expressed as: .

[0032] in, represents the speed of the leader car in the k-th queue at time t, represents the speed of the last vehicle in the k-th queue at time t.

[0033] For each vehicle i in the queue, the distance between it and the previous vehicle is fixed. To achieve coupling control, each queue is defined to be connected to the adjacent queue by a fixed distance constraint in order to ensure the coordinated control among multiple queues. coupling.

[0034] Hypothetical state output definition: Based on the optimal solution at the previous moment, a hypothetical state output is generated to predict the system behavior at the next moment.

[0035] ; ; in: express t The optimal state sequence at time, Represents the hypothetical state sequence for the next moment; Terminal Constraint Design: Terminal state constraint design defines the terminal state constraints of all vehicles to ensure that the state at the end of the prediction time domain can meet the queue synchronization requirements and guarantee recursive feasibility.

[0036] ; Where: Representation Queue k No. i A car in t The terminal prediction state at time Representation Queue k No. i The car in front t The terminal hypothetical state at time, Indicates the minimum safe distance between vehicles.

[0037] Prediction horizon setting: According to the vehicle dynamics characteristics and the dynamic behavior of the queue, the length of the prediction horizon is determined so that the controller can optimize for a sufficiently far future period.

[0038] Cost function design: State error cost function design. Set the state error cost function to control the state error, control input, assumed state error, and leading vehicle distance error respectively to ensure the stability and coordination of vehicles in the queue, penalize the error of vehicles deviating from the expected state, and optimize the state consistency inside and outside the queue during path tracking.

[0039] 1. Control state error: ; 2. Control input size: ; 3. Assuming state error ; 4. Distance error of the vehicle ahead ; Where: represents the predicted state sequence, represents the hypothetical state sequence, represents the hypothetical state sequence of the preceding vehicle, represents the expected state sequence, represents the predictive control input sequence, Indicates vehicle i Distance error to the vehicle ahead.

[0040] Control input constraint definition. Add physical limits on control inputs (such as maximum acceleration, maximum steering angle) to ensure that the solution to the optimization problem can meet the actual vehicle operating conditions.

[0041] ; Where: represents the minimum control input, Indicates the maximum control input.

[0042] In another embodiment of the present invention, the vehicle node local optimization problem includes a vehicle local cost function and vehicle constraints, and the vehicle constraints include a vehicle prediction model constraint, an initial predicted state equal to the actual state constraint at time t, a vehicle state constraint, a vehicle control input constraint, a vehicle position constraint, and recursive feasibility and stability constraints.

[0043] The expression of the local optimization problem of the vehicle node includes: ; Specifically, in the formula: express k Vehicle nodes in the queue i Cost function for local optimization problems; represents the prediction time domain; express k Vehicle nodes in the queue i exist t The state of the moment, and They represent the vehicle node i at t The predicted state and predicted control input sequence at time t, represents the assumed state of vehicle node i at time t, represents the hypothetical state of the preceding vehicle of vehicle node i at time t; , express k Vehicle nodes in the queue i exist t Control input at any time.

[0044] The constraint formulas represent the vehicles i The constraints of the prediction model; the initial prediction state is equal to the moment t Constraints on actual state; queues k Medium Vehicle i State constraints of queues k Medium Vehicle i Input constraints; for vehicles i Location track Set to ensure that spatial formation constraints within the queue are met; ensure recursive feasibility and stability constraints.

[0045] More specifically, the control target definition within the queue is set to follow the front car, maintain a fixed safe distance, and gradually approach the speed of the leading car to achieve stability within the queue.

[0046] ; Where: Indicates the safe distance between vehicles.

[0047] In another embodiment of the present invention, the local cost function comprises: ; Where: and They represent the predicted state and predicted control input sequence of vehicle node i at time t, represents the assumed state of vehicle node i at time t; represents the hypothetical state of the preceding vehicle at vehicle node i at time t, Represents the distance between vehicle node i and the preceding vehicle; weight matrix are set to be symmetric non-negative definite matrices, representing the control state error, control input, assumed state error, and leading vehicle distance error, respectively.

[0048] Specifically, and They represent the predicted state and predicted control input sequence of vehicle node i at time t, represents the assumed state of vehicle node i at time t; represents the hypothetical state of the preceding vehicle at vehicle node i at time t, Represents the distance between vehicle node i and the preceding vehicle.

[0049] In another embodiment of the present invention, the queue global optimization problem includes a queue global cost function and queue constraints, the queue constraints include communication topology constraints, queue position constraints and queue length change constraints, the communication topology constraints are used to constrain the state update of the leader vehicle to depend on the state information of other leader vehicles defined by the communication topology, ensuring that the behavior of the leader vehicle is restricted by the communication graph structure and can only obtain information from neighbors in the communication topology, and the queue position constraints are used to set the position trajectories of the leader vehicle and the tail vehicle in the queue to ensure that the space constraints between queues are met.

[0050] The cost function of the queue global optimization problem includes: ; Where: For queue k The amount of information received by the pilot car Integration of multi-objective optimization problems. Integrate local optimization goals with global optimization goals into multi-objective optimization problems to ensure the global coordination of the system.

[0051] ; Where: are the weight coefficients of the coordinated control of node i and queue k, represents the cost function of the local optimization problem of queue k, Cost function of the local optimization problem of vehicle nodes.

[0052] In another embodiment of the present invention, the controller is modularly designed, and the distributed model predictive controller is divided into a local optimization module, an information sharing module and a state update module, which is convenient for actual system implementation.

[0053] Specifically, the control objectives between queues are defined to ensure that adjacent pilot vehicles between queues maintain a fixed safety distance and synchronize their speeds to avoid collisions and incoordination between queues.

[0054] ;

[0055] In another embodiment of the present invention, the queue global cost function includes: ; Where: represents the predicted state sequence of the pilot vehicle; represents the predictive control input sequence of the pilot vehicle, represents the distance error between adjacent pilot vehicles in the queue, represents the distance error between the leading vehicle in queue k and the last vehicle in the previous queue, represents the k-1 queue length; represents the distance error between adjacent pilot vehicles in the queue, represents the distance between the leading vehicle in queue k and the last vehicle in the previous queue; It is set as a symmetric non-negative definite matrix, which controls the state error, control input, assumed state error and leading vehicle distance error respectively to ensure the stability and coordination of vehicles in the queue.

[0056] Weight Matrix It is set as a symmetric non-negative definite matrix, which controls the state error, control input, assumed state error and leading vehicle distance error respectively to ensure the stability and coordination of vehicles in the queue.

[0057] In another embodiment of the present invention, the step of S400 includes: S410, vehicle status, input status and queue length initialization; S420, for each platoon, generating an expected trajectory of the pilot vehicle in the prediction time domain; S430. At each moment, solve the optimization control problem for each vehicle node, and obtain the optimal control input sequence based on the optimization result to achieve coordinated control within and between queues.

[0058] Specifically, initialization Vehicle status initialization: Set the initial status of each vehicle, including the specific status of the pilot vehicle and the following vehicles. Initial time When , the state of each vehicle is expressed as: ;

[0059] in, Assume that the initial speed of each vehicle in the queue is , ensure that the distance between vehicles meets the minimum safety distance , the queue spacing meets the minimum safety spacing .

[0060] S410, input state initialization: Initialize the hypothetical input sequence of each node and the assumed state sequence , used to predict future inputs and states.

[0061] Queue length initialization: define queue length is the distance between the leading car and the trailing car, at the initial moment: ,in: represents the number of vehicles in the queue, The queue spacing meets the minimum safety spacing.

[0062] S420, generating the expected trajectory: For each queue k, generating the expected trajectory of the pilot vehicle in the prediction time domain , in order to guide the coordinated control of the entire queue. The expected trajectory sequence is: ;

[0063] in, , represents the expected position of the platoon leader vehicle, Indicates the expected speed of the platoon leader vehicle, ensuring that the platoon leader vehicle can maintain synchronization based on the distance between platoons.

[0064] S430, Optimization control problem solving At every moment t , for each vehicle node i Solve the optimization control problem and obtain the optimal control input sequence ,in .

[0065] In another embodiment of the present invention, the step of S430 includes: S431, the queue status update step, the node status update step and the information exchange and synchronization step are continuously cycled at each moment, and the control input and status of all queues and vehicle nodes are gradually updated to achieve coordinated control within and between queues; S432, the queue state updating step includes: updating the state sequence of the pilot vehicle to ensure that it follows the expected trajectory; in the prediction time domain, according to the optimal control input sequence, updating the state of each vehicle, and calculating the next step of the pilot vehicle's assumed input sequence and updating the assumed state sequence; S433, the node state updating step includes: using the optimal control input to update the current node state, calculating the optimal state sequence and updating the node state in the prediction time domain, updating the assumed input sequence of the vehicle, and predicting the assumed state sequence based on the assumed input sequence; S434, the information exchange and synchronization step includes: sending the queue leader vehicle node assumed state sequence and assumed input sequence to the adjacent leader vehicle node, and sending the node assumed state sequence and assumed input sequence to the adjacent follower vehicle node to achieve information sharing.

[0066] Specifically, the solution process: S431, loop iteration: At every moment t , from S432 (Step 1) to S434 (Step 3) continuously loop, gradually updating the control input and status of all queues and vehicle nodes until the entire system reaches a synchronized and stable state.

[0067] S432 (Step 1): Queue status update: (1) Update the state sequence of the pilot vehicle to ensure it follows the desired trajectory.

[0068] (2) In the prediction time domain, according to the optimal input Update the status of each vehicle.

[0069] (3) Calculate the next hypothetical input sequence of the pilot vehicle And update the hypothetical state sequence ; Queue length sequence .

[0070] S433 (Step 2): Node status update: (1) Use optimal control input Update the current node status.

[0071] (2) Calculate the optimal state sequence within the prediction time domain And update the node status.

[0072] (3) Update the vehicle’s hypothetical input sequence .

[0073] (4) Based on the assumption that the input sequence Predict a sequence of hypothetical states: .

[0074] S434 (Step 3): Information exchange and synchronization: (1) Assume the state sequence of the queue leader node and assume the input sequence Send to neighboring pilot car nodes.

[0075] (2) Assume the node state sequence and assume the input sequence The information is sent to the neighboring follower nodes to achieve information sharing and ensure the overall optimization effect.

[0076] In another embodiment of the present invention, after step S200, the following steps are included: S210, establishing a plurality of communication strategies, wherein the communication strategies include a leader vehicle to leader vehicle strategy, a leader vehicle to tail vehicle strategy, and a leader vehicle to tail vehicle strategy; the leader vehicle to leader vehicle strategy includes a communication mode in which information is transmitted step by step through the leader vehicle of the queue, the leader vehicle to tail vehicle strategy includes a communication mode in which the leader vehicle directly receives information from the tail vehicle of the previous queue, and the leader vehicle to tail vehicle strategy includes a communication mode in which the leader vehicle is allowed to simultaneously receive information from the leader vehicle and tail vehicle of the previous queue; S220, selecting different communication strategies according to queue length, response speed and dynamic environment.

[0077] Specifically, a variety of communication strategies are adapted to diverse needs: providing lead vehicle to lead vehicle, lead vehicle to tail vehicle and comprehensive communication strategies, which are respectively suitable for application scenarios of medium scale, rapid response and complex dynamic environment, providing flexible solutions for fleet control with different needs.

[0078] Communication topology design (within the queue). Design the communication topology structure within a single queue, use the pilot car as the root node to build a spanning tree, and ensure that each car in the queue can obtain the status information of its preceding car through communication. Specifically: Establish a one-way communication topology structure with the pilot car of each queue as the core, including the internal communication topology of the queue and the communication topology between queues. In each queue k The present invention considers a directed graph ,in It is a queue k The set of communication edges between different vehicles in the graph. Middle Edge Indicates vehicle i Can receive vehicles j The state information transmitted, the present invention defines For the k Queue No. i The following vehicle set that the following vehicle can receive information from. In addition, in order to consider the communication topology of the pilot vehicle, the present invention defines a graph Contains a directed spanning tree with all pilot vehicles as root nodes, where is the set of pilot vehicle nodes in the platoon system, It is a collection of communication edges between queues in a queue system to ensure information transmission between queues.

[0079] definition The set of vehicles that the leader car of each platoon can receive information from. Exchange information and pass the received cross-queue information to the following vehicles in the same queue, thereby realizing collaborative control among multiple queues.

[0080] The communication topology within the queue is a directed spanning tree with the pilot vehicle as the root node. set up For queue k The adjacency matrix of in Indicates j The following car can i The following car sends information.

[0081] All nodes form a directed spanning tree starting from the root node 0, ensuring that all following vehicles can directly or indirectly obtain the status information of the pilot vehicle.

[0082] Then the adjacency matrix It can be expressed as: ;

[0083] Defining Queues k The Laplace matrix of To represent the connection relationship between vehicles in the queue, it is composed of the in-degree matrix and the adjacency matrix Constructed, expressed as ,in Indicates the amount of information each node receives from other nodes.

[0084] Then the Laplacian matrix of the communication within the queue is Defined as: ; in, Representation Queue k The amount of information received by the pilot car.

[0085] Communication topology design (inter-queue): Design the communication topology of the pilot vehicles between platoons, and use a directed graph to represent the information transmission path between the pilot vehicles to ensure collaborative control across platoons.

[0086] Specifically, the pilot car of each queue can receive specific information from the previous queue. To this end, for different strategies, the communication matrix between queues is defined , and define the system global adjacency matrix and the global Laplacian matrix .

[0087] Definition of the global state of the system. The state set of the entire multi-queue system is defined as the global state vector, including the states of all vehicles (position, velocity, acceleration) and the state coupling between the queues.

[0088] Platoon state update rules. Specify the update rules for the vehicle states at each time step in the system model to ensure that the state update can take into account the dynamic effects of communication topology and control inputs.

[0089] Information exchange rules between queues. Design an information exchange mechanism between pilot vehicles to ensure that the pilot vehicle of each queue can obtain relevant status information of other queues in a timely manner. The details include the following: Strategy a: Pilot-pilot tracking strategy. The pilot car of each queue only receives information from the pilot car of the previous queue. Then the communication matrix between queues is It can be expressed as: ;

[0090] The adjacency matrix is: ;

[0091] The Laplace matrix is: ;

[0092] Strategy b: Leading car-tail car tracking strategy. Only the leading car in each queue receives the tail car information of the previous queue. Therefore, there is a communication connection between the leading car and the tail car. Strategy b has the same structure of adjacency matrix and Laplace matrix as strategy a, but the difference is the representation of the communication matrix between queues, which can be expressed as: ;

[0093] Strategy c: The combination of the leader car and the leader car-tail car strategy. Combining the above two strategies, the leader car can simultaneously receive information from the leader car and the tail car of the previous queue. Therefore, the communication matrix between its queues is expressed as: ;

[0094] Pilot to pilot strategy (Strategy a): Information is transmitted step by step through the queue pilot, which has high consistency and low communication load and is suitable for medium-length queues. However, due to the characteristics of step-by-step information transmission, longer queues may experience response lags, limiting the dynamic response capability of the system.

[0095] Leading car to tail car strategy (Strategy b): The leading car directly receives information from the tail car in the previous queue, which enhances the dynamic response capability of the system and is suitable for small-scale scenarios that require rapid response. However, as the size of the queue increases, this strategy performs poorly in terms of stability.

[0096] Leading car to tail car strategy (Strategy c): This strategy combines the first two methods, allowing the leading car to receive information from the leading car and tail car in the previous queue at the same time, thus taking into account both global and local state changes, balancing responsiveness and stability, and is very suitable for complex dynamic environments that require high consistency and fast response. However, its communication complexity is relatively high.

[0097] The method proposed in this application can effectively improve the global coordination and adaptability of the multi-queue system under complex dynamic conditions, and provide a robust solution for realizing the coordinated control of multi-vehicle queues.

[0098] In another embodiment of the present invention, executable instructions are stored, and the executable instructions include program codes for executing the multi-vehicle platoon cooperative control method based on distributed model predictive control as described in any one of the above items when executed.

[0099] In another embodiment of the present invention, in order to verify the effectiveness of the present invention in a multi-vehicle platoon system, the performance of the established model and algorithm is evaluated through numerical simulation.

[0100] The following is the specific verification analysis process and results: Simulation background: The initial condition is set to have no initial displacement error and velocity error for all vehicles to ensure that the system is in equilibrium at the start. The reference trajectory of the pilot vehicle is designed as an acceleration-deceleration cycle to simulate the acceleration and braking behavior of the vehicle in actual operation. Specifically, the pilot vehicle performs uniform acceleration at an acceleration of 2 m / s² within 1 to 2 seconds to increase the overall speed of the queue; and uniform deceleration at an acceleration of -2 m / s² within 3 to 4 seconds to reduce the speed of the vehicle. This reference trajectory provides a stable following benchmark for subsequent vehicles, thereby ensuring the response stability and queue consistency of the system under dynamically changing conditions.

[0101] Parameter setting: This simulation sets up three queues (red 9, blue 7, green 7), with a total of 23 vehicles, a vehicle mass of 1500kg, a driving delay of 0.7 s, an air resistance coefficient of 1, and a tire radius of 0.35 m.

[0102] The system time step is 0.1 s, the friction coefficient is 0.01, the transmission efficiency is 0.96, the gravity acceleration is 9.8 m / s², and the prediction time domain is 20 steps. The expected distance between vehicles is set to 10 m, and the expected distance between queues is 30 m. , .

[0103] The remaining distributed model predictive control (DMPC) controller weight matrices are set as follows Fig.16 These parameters ensure the effectiveness and accuracy of multi-queue system simulation under different strategies.

[0104] Strategy A: Pilot car-pilot car tracking strategy ( Figure 1 ). The pilot car of each queue only receives the pilot car information of the previous queue. , , the communication matrix between queues And the system global adjacency matrix can be expressed as: ; ; In strategy a, the communication topology is hierarchical, with vehicles in the queue forming a chain communication, and cross-queue communication only between the pilot vehicles. This design ensures that information is transmitted from the pilot vehicle in the front queue to the pilot vehicle in the rear queue step by step, thus building a multi-level control system with the pilot vehicle as the core. Figure 2-Figure 5It can be seen that the communication structure effectively reduces the communication load of the system. At the same time, the step-by-step transmission method enhances the overall stability of the queue, making the system respond consistently in terms of speed, position, torque and acceleration, which is suitable for large-scale fleet collaborative control with high consistency requirements.

[0105] Strategy b: Leading car-trailing car tracking strategy ( Figure 6 ). Only the leading car in each queue receives the information of the last car in the previous queue. , Therefore, there is a communication connection between the leading car and the trailing car. The adjacency matrix of strategy b has the same structure as that of strategy a, but the difference is the representation of the communication matrix between queues, which can be expressed as: ; In contrast, in strategy b, the lead car obtains information from the tail car, so that the next queue can respond to the state changes at the tail of the previous queue more promptly, thereby improving the safety and response speed of the system. Figure 7-Figure 10 , it can be seen that this design improves the sensitivity of the queue to the movement changes of the lead car, but because the state of the tail car is easily affected by many factors, it increases uncertainty and may have an adverse effect on the control stability of the next queue leader car. Therefore, strategy b is more suitable for scenarios with smaller scale and moderate queue length. In this case, the uncertainty of the tail car state will not be significantly amplified, and the information transmission delay is also small, which is conducive to the system to respond quickly to changes ahead and achieve higher dynamic adaptability and safety.

[0106] Strategy C: Leading car and leading car-tailing car combination strategy ( Fig.11 ).

[0107] Strategy C combines the above two strategies. The leading car can receive information from the leading car and the tail car of the previous queue at the same time. Therefore, the communication matrix between queues is expressed as: ; In strategy c, the leading car can simultaneously obtain the status information of the leading car and the tail car in the previous queue, thereby realizing dual perception of global and local changes. Figure 12-Figure 15 It can be seen that the communication structure of strategy c improves the control accuracy of the queue, reduces the impact of information lag, and enables the system to respond to the dynamic changes of the queue in front in a timely manner. The results show that strategy c performs well in speed and position control, with high consistency of speed curves of each queue, stable position relationship, and small changes in the distance between vehicles and queues.

[0108] In general, strategy c achieves an excellent balance between response speed and stability, and is suitable for high-density, dynamic and complex environments. Compared with strategies a and b, strategy c achieves a better balance between queue consistency and dynamic response, but it also increases the complexity of communication. Therefore, this strategy is particularly suitable for multi-queue collaboration scenarios with high consistency and fast response requirements.

[0109] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. All equivalent structural changes made using the contents of the present invention's specification and drawings, or directly / indirectly applied in other related technical fields, are included in the patent protection scope of the present invention.

Claims

1. A multi-vehicle platoon cooperative control method based on distributed model predictive control, characterized in that: The steps include: Constructing a multi-vehicle platoon system and a dynamic model, wherein the dynamic model includes a system state vector, a motion state equation of a single vehicle, and a platoon length state equation; Establishing a unidirectional communication topology structure, wherein the communication topology structure includes an internal queue communication topology and an inter-queue communication topology, wherein the internal queue communication topology includes a directed spanning tree with a pilot vehicle as a root node, and the inter-queue communication topology includes a directed spanning tree with a pilot vehicle of a first queue of the system as a root node; Based on the communication topology, coupling constraints between vehicles and queues are defined, and a distributed model predictive control optimization problem and a cost function are constructed. The distributed model predictive control optimization problem includes a local optimization problem for vehicle nodes, a global optimization problem for queues, and a multi-objective optimization problem integrating the local optimization problem for vehicle nodes and the global optimization problem for queues. The local optimization problem for vehicle nodes is used to solve the local optimal control input for each vehicle node, and the global optimization problem for queues is used to solve the coordinated control input between queues for each queue node using the multi-objective optimization problem. The optimization control problem is solved for each vehicle node, and the control input and motion state of each vehicle are adjusted according to the optimization results to achieve coordinated control within and between queues.

2. The multi-vehicle platoon cooperative control method based on distributed model predictive control according to claim 1, characterized in that: The steps of constructing a multi-vehicle platoon system and a dynamic model, wherein the dynamic model includes a system state vector, a motion state equation of a single vehicle, and a platoon length state equation, include: defining the update rules of the vehicle state and vehicle state variables, wherein the vehicle state vector includes position, velocity, acceleration and torque, and constructing the dynamic equations of a single vehicle using a discrete time model; The queue length is defined as the distance between the leading car and the tail car, and an equation for the dynamic change of the queue length is established.

3. The multi-vehicle platoon cooperative control method based on distributed model predictive control according to claim 1, characterized in that: The vehicle node local optimization problem includes a vehicle local cost function and vehicle constraints, wherein the vehicle constraints include a vehicle prediction model constraint, an initial prediction state equal to the actual state constraint at time t, a vehicle state constraint, a vehicle control input constraint, a vehicle position constraint, and recursive feasibility and stability constraints.

4. The multi-vehicle platoon cooperative control method based on distributed model predictive control as claimed in claim 3, characterized in that: The vehicle local cost function includes: ； Where: and They represent the predicted state and predicted control input sequence of vehicle node i at time t, represents the assumed state of vehicle node i at time t; represents the hypothetical state of the preceding vehicle at vehicle node i at time t, Represents the distance between vehicle node i and the preceding vehicle; weight matrix are set to be symmetric non-negative definite matrices, representing the control state error, control input, assumed state error, and leading vehicle distance error, respectively.

5. The multi-vehicle platoon cooperative control method based on distributed model predictive control according to claim 1, characterized in that: The queue global optimization problem includes a queue global cost function and queue constraints. The queue constraints include communication topology constraints, queue position constraints and queue length change constraints. The communication topology constraints are used to constrain the state update of the leader vehicle to depend on the state information of other leader vehicles defined by the communication topology, ensuring that the behavior of the leader vehicle is restricted by the communication graph structure and can only obtain information from neighbors in the communication topology. The queue position constraints are used to set the position trajectories of the leader vehicle and the tail vehicle in the queue to ensure that the space constraints between queues are met.

6. The multi-vehicle platoon cooperative control method based on distributed model predictive control as claimed in claim 5, characterized in that: The queue global cost function includes: ； In the formula, represents the predicted state sequence of the pilot vehicle; represents the predictive control input sequence of the pilot vehicle, represents the k-1 queue length; represents the distance error between adjacent pilot vehicles in the queue, represents the distance between the leading vehicle in queue k and the last vehicle in the previous queue; It is set as a symmetric non-negative definite matrix, which controls the state error, control input, assumed state error and leading vehicle distance error respectively to ensure the stability and coordination of vehicles in the queue.

7. The multi-vehicle platoon cooperative control method based on distributed model predictive control as claimed in claim 1, characterized in that: The step of adjusting the motion state of each vehicle according to the optimization result, the motion state including position, speed and acceleration, to achieve coordinated control within and between queues includes: Vehicle status, input status and queue length initialization; For each platoon, the expected trajectory of the leader vehicle is generated in the prediction time domain; At each moment, the optimization control problem is solved for each vehicle node, and based on the optimization results, the optimal control input sequence is obtained to achieve coordinated control within and between queues.

8. The multi-vehicle platoon cooperative control method based on distributed model predictive control according to claim 1, characterized in that: The steps of solving the optimization control problem for each vehicle node at each moment and obtaining the optimal control input sequence to achieve the coordinated control within and between the queues include: The queue status update step, node status update step, and information exchange and synchronization step are continuously circulated at each moment, gradually updating the control input and status of all queues and vehicle nodes to achieve coordinated control within and between queues; The queue state updating step includes: updating the state sequence of the pilot vehicle to ensure that the desired trajectory is followed; updating the state of each vehicle according to the optimal control input sequence within the prediction time domain, and calculating the next step hypothetical input sequence of the pilot vehicle and updating the hypothetical state sequence; The node state updating step includes: updating the current node state using the optimal control input, calculating the optimal state sequence and updating the node state in the prediction time domain, updating the assumed input sequence of the vehicle, and predicting the assumed state sequence based on the assumed input sequence; The information exchange and synchronization steps include: sending the assumed state sequence and assumed input sequence of the queue leader vehicle node to the adjacent leader vehicle node, and sending the node assumed state sequence and assumed input sequence to the adjacent follower vehicle node to achieve information sharing.

9. The multi-vehicle platoon cooperative control method based on distributed model predictive control as claimed in claim 1, characterized in that: After the step of establishing a unidirectional communication topology structure, wherein the communication topology structure includes an internal queue communication topology and an inter-queue communication topology, wherein the internal queue communication topology includes a directed spanning tree with the pilot vehicle as the root node, and the inter-queue communication topology includes a directed spanning tree with the pilot vehicle of the first queue of the system as the root node, the following steps are included: Establish multiple communication strategies, including a leader-to-leader strategy, a leader-to-last-car strategy, and a leader-to-last-car strategy; the leader-to-leader strategy includes a communication method in which information is transmitted step by step through the leader car of the queue, the leader-to-last-car strategy includes a communication method in which the leader car directly receives information from the last car of the previous queue, and the leader-to-last-car strategy includes a communication method in which the leader car is allowed to simultaneously receive information from the leader car and the last car of the previous queue; Select different communication strategies based on queue length, response speed, and dynamic environment.

10. A multi-vehicle platoon cooperative control medium based on distributed model predictive control, characterized in that: Executable instructions are stored, and the executable instructions include program codes for executing the multi-vehicle platoon cooperative control method based on distributed model predictive control according to any one of claims 1 to 9 when executed.

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