Motorcade consistency formation control method based on dynamic event triggering mechanism

Abstract

The invention relates to a motorcade consistency formation control method and device based on a dynamic event triggering mechanism, electronic equipment and a storage medium. The method comprises the steps of generating a communication topological graph of a multi-agent system composed of a preset number of unmanned vehicles according to the theoretical basis of a graph theory, and building a mathematical model of each unmanned vehicle; based on the mathematical model, designing a combined measurement function, a measurement error and a consistency controller of the multi-agent system; distributed event triggering conditions and self-triggering conditions are designed based on Lyapunov parameters; according to a dynamic event triggering condition and a self-triggering condition, a triggering time interval expression of the intelligent agent is obtained through calculation; by constructing a multi-agent system consistency controller and combining distributed event triggering conditions and self-triggering conditions, multi-vehicle formation consistency control is completed. According to the fleet consistency formation control method disclosed by the invention, accurate and efficient control of a fleet formed by multiple unmanned vehicles can be realized.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the field of multi-agent system consensus control, and in particular, to a vehicle formation consistency control method and device based on a dynamic event triggering mechanism, an electronic device, and a computer readable storage medium. BACKGROUND

[0002] A multi-agent formation control system is a large system composed of multiple agents (robots, sensor nodes, unmanned vehicles, or unmanned aerial vehicles, etc.), which has the characteristics of distribution, dynamics, nonlinearity, and complexity. Each agent in the system has the functions of perception, calculation, execution, and communication, and the agents communicate and interact with each other, and finally complete the target task together. Compared with a single complex system, a multi-agent system can improve the efficiency of task execution and increase the redundancy and risk resistance of the system. However, due to the different perception, calculation, and execution capabilities of the agents in the system, as well as the communication delay and uncertainty, it is difficult for the agents to maintain consistent behavior or achieve consistent goals. Therefore, consensus control has become an important direction in the research of multi-agent formation control systems.

[0003] Vehicle consistency formation control aims to achieve cooperative decision-making and cooperative driving among vehicles to improve the safety, efficiency, and stability of the formation. Through communication and state information interaction among intelligent vehicles, a certain lateral and longitudinal spacing and pose state are maintained to form a formation and complete the task together. Some of the current research designs a consensus control protocol that requires a large amount of information exchange and high computational load, which has high requirements for system configuration and hardware devices. However, in actual systems, due to limited communication bandwidth and computational resources, and the requirement that the system forms a formation within a specified time, it is necessary to introduce an event-triggered control strategy. The agents in the system only interact with each other and update the state when a specific event occurs, which can reduce communication overhead and computational load, improve the efficiency and stability of the system, especially in resource-constrained or high-communication-cost situations, and can improve the scalability and adaptability of the system.

[0004] In addition, in some multi-agent formation control research based on event-triggered control strategies, an event-triggered function is often defined, and the sampling value and the controller are updated and the information is exchanged with the neighbors when and only when the event-triggered function meets certain conditions. However, when the number of neighbors is large, both the individual and the neighbors need to update the controller, and the controller update frequency is high. It is necessary to design a new trigger control strategy to predict the next trigger time at the current time without communication with the neighbors in between.

[0005] Therefore, it is of great significance to propose advanced dynamic event-triggered control strategies to study methods to improve the convergence speed of formation control and save limited communication resources.

[0006] It should be noted that the information disclosed in the above BACKGROUND section is only for the purpose of strengthening the understanding of the background of the present disclosure, and therefore can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0007] The purpose of the present disclosure is to provide a vehicle team consistency formation control method and device based on a dynamic event triggering mechanism, electronic equipment and computer readable storage medium, thereby at least to some extent overcoming one or more problems caused by the limitations and defects of the related art.

[0008] According to one aspect of the present disclosure, a vehicle team consistency formation control method based on a dynamic event triggering mechanism is provided, comprising:

[0009] According to the graph theory basis, a communication topology graph of a multi-agent system composed of a preset number of unmanned vehicles is generated, and a mathematical model of each unmanned vehicle is built;

[0010] Based on the mathematical model, a combined measurement function of the multi-agent system is designed, and a measurement error and a consistency controller are measured;

[0011] Based on the Lyapunov parameter, a distributed event triggering condition and a self-triggering condition are designed;

[0012] According to the dynamic event triggering condition and the self-triggering condition, a triggering time interval expression of the agent is calculated;

[0013] By constructing a multi-agent system consistency controller, combining the distributed event triggering condition and the self-triggering condition, the multi-vehicle formation consistency control is completed.

[0014] In an exemplary embodiment of the present disclosure, the method further comprises:

[0015] According to the graph theory basis, a communication topology graph of a multi-agent system composed of a preset number of unmanned vehicles is generated according to the speed, heading angle and communication state of each vehicle in the multi-unmanned vehicle system, and then an adjacency matrix and a Laplace matrix are calculated through the topology graph, and a mathematical model of each unmanned vehicle is built.

[0016] In an exemplary embodiment of the present disclosure, the method further comprises:

[0017] The multi-agent system composed of a preset number of unmanned vehicles is composed of a leading vehicle and a plurality of following vehicles, and an adjacency matrix and a Laplace matrix are calculated through the topology graph, and a dynamics model of each unmanned vehicle is built.

[0018] In an exemplary embodiment of the present disclosure, the method further comprises:

[0019] Based on the mathematical model, a combined measurement function for the multi-agent system is designed, and an event-triggered measurement error and a distributed multi-vehicle platooning consistency controller based on the event-triggered mechanism are designed based on the combined measurement function.

[0020] In one exemplary embodiment of this disclosure, the method further includes:

[0021] Based on Lyapunov parametric design, a distributed dynamic event-triggered formation control strategy and a distributed self-triggered formation control strategy are proposed.

[0022] In one exemplary embodiment of this disclosure, the method further includes:

[0023] The initialization event triggers the consistency controller, which obtains the initial value of the cooperative state information of adjacent vehicles through the topology communication network;

[0024] The estimated values ​​of the motion state information of adjacent vehicles are obtained by substituting the initial values ​​of the vehicle cooperative state information into the state observer calculation.

[0025] In one exemplary embodiment of this disclosure, the method further includes:

[0026] The trigger function value is calculated based on the estimated motion state of the following vehicle and the motion state information of the lead vehicle. It is then determined whether the communication trigger condition is met. If it is met, a communication is performed to obtain the cooperative status information of the nearby vehicle; otherwise, no communication is performed.

[0027] Each vehicle uses the cooperative state information to input the state observer to obtain the motion state estimates of the leader and followers, calculates the formation consistency control sequence, and transmits it to the vehicle motion controller for execution.

[0028] In one aspect of this disclosure, a fleet consistency formation control device based on a dynamic event triggering mechanism is provided, comprising:

[0029] The mathematical modeling module is used to generate a communication topology diagram of a multi-agent system composed of a preset number of unmanned vehicles, based on graph theory, and to build a mathematical model for each unmanned vehicle.

[0030] The measurement function design module is used to design the combined measurement function, measurement error and consistency controller of the multi-agent system based on the mathematical model.

[0031] The trigger condition design module is used to design distributed event trigger conditions and self-trigger conditions based on Lyapunov parameters.

[0032] The interval expression calculation module is used to calculate the trigger time interval expression of the agent based on the dynamic event triggering conditions and self-triggering conditions.

[0033] The consistency control module is used to construct a multi-agent system consistency controller and combine distributed event triggering conditions and self-triggering conditions to complete the consistency control of multi-vehicle platooning.

[0034] In one aspect of this disclosure, an electronic device is provided, comprising:

[0035] Processor; and

[0036] A memory storing computer-readable instructions that, when executed by the processor, implement the method according to any one of the preceding claims.

[0037] In one aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the method according to any one of the preceding claims.

[0038] An exemplary embodiment of this disclosure discloses a vehicle fleet consistency formation control method based on a dynamic event triggering mechanism. The method includes: generating a communication topology graph of a multi-agent system composed of a preset number of unmanned vehicles based on graph theory, and constructing a mathematical model for each unmanned vehicle; designing a combined measurement function, measurement error, and consistency controller for the multi-agent system based on the mathematical model; designing distributed event triggering conditions and self-triggering conditions based on Lyapunov parameters; calculating the triggering time interval expression for each agent according to the dynamic event triggering conditions and self-triggering conditions; and completing multi-vehicle fleet consistency control by constructing a multi-agent system consistency controller and combining the distributed event triggering conditions and self-triggering conditions. This disclosure proposes a dynamic event triggering strategy requiring continuous communication and a self-triggering strategy that avoids continuous communication. Both control strategies effectively avoid Zeno behavior. In the designed control strategies, individuals only actively acquire neighbor information and update the controller when they trigger themselves, thus reducing the number of controller updates. During the triggering function design process, both fleet error and state estimation error are considered, making the proof process more rigorous and improving the convergence speed of the state observer and the fleet accuracy.

[0039] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0040] The above and other features and advantages of this disclosure will become more apparent from the detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0041] Figure 1 A flowchart of a fleet consistency formation control method based on a dynamic event triggering mechanism according to an exemplary embodiment of the present disclosure is shown;

[0042] Figure 2 A schematic diagram of the navigator and follower topology of a fleet consistency formation control method based on a dynamic event triggering mechanism according to an exemplary embodiment of the present disclosure is shown.

[0043] Figure 3 A block diagram of a finite-time vehicle formation control system based on an event-triggered mechanism is shown, according to an exemplary embodiment of the present disclosure, for a fleet consistency formation control method based on a dynamic event-triggered mechanism.

[0044] Figure 4 This illustration demonstrates the tracking error under a finite-time control strategy of a fleet consistency formation control method based on a dynamic event triggering mechanism, according to an exemplary embodiment of the present disclosure. Schematic diagram of the change curve;

[0045] Figure 5 This illustration demonstrates a finite-time control strategy for a fleet consistency formation control method based on a dynamic event-triggered mechanism, according to an exemplary embodiment of the present disclosure, involving combined measurement q. i (t) Schematic diagram of the change curve;

[0046] Figure 6 A schematic diagram of the motion trajectory of an unmanned vehicle under a finite-time control strategy for a fleet consistency formation control method based on a dynamic event triggering mechanism, according to an exemplary embodiment of the present disclosure, is shown.

[0047] Figure 7 This illustration demonstrates the tracking error under an event-triggered control strategy in a fleet consistency formation control method based on a dynamic event-triggered mechanism, according to an exemplary embodiment of the present disclosure. Schematic diagram of the change curve;

[0048] Figure 8 This invention illustrates a combined measurement q under an event-triggered control strategy of a fleet consistency formation control method based on a dynamic event-triggered mechanism, according to an exemplary embodiment of the present disclosure. i (t) Schematic diagram of the change curve;

[0049] Figure 9 The diagram illustrates the trajectory of an unmanned vehicle under an event-triggered control strategy of a fleet consistency formation control method based on a dynamic event-triggered mechanism, according to an exemplary embodiment of the present disclosure.

[0050] Figure 10 This illustration demonstrates the tracking error under a self-triggered control strategy of a fleet consistency formation control method based on a dynamic event-triggered mechanism, according to an exemplary embodiment of the present disclosure. Schematic diagram of the change curve;

[0051] Figure 11This invention illustrates a combined measurement q under a self-triggered control strategy of a fleet consistency formation control method based on a dynamic event triggering mechanism, according to an exemplary embodiment of the present disclosure. i (t) Schematic diagram of the change curve;

[0052] Figure 12 The diagram illustrates the motion trajectory of an unmanned vehicle under a self-triggered control strategy of a fleet consistency formation control method based on a dynamic event triggering mechanism, according to an exemplary embodiment of the present disclosure.

[0053] Figure 13 The diagram illustrates the triggering times of three vehicles under a self-triggering strategy of a fleet consistency formation control method based on a dynamic event triggering mechanism, according to an exemplary embodiment of the present disclosure.

[0054] Figure 14 The diagram illustrates the triggering times of three vehicles under a dynamic event triggering strategy in a fleet consistency formation control method based on a dynamic event triggering mechanism, according to an exemplary embodiment of the present disclosure.

[0055] Figure 15 The diagram illustrates the triggering times of three vehicles under a conventional event-triggered strategy for a fleet consistency formation control method based on a dynamic event-triggered mechanism, according to an exemplary embodiment of the present disclosure.

[0056] Figure 16 A schematic block diagram of a fleet consistency formation control device based on a dynamic event triggering mechanism according to an exemplary embodiment of the present disclosure is shown.

[0057] Figure 17 A block diagram of an electronic device according to an exemplary embodiment of the present disclosure is shown schematically;

[0058] Figure 18 The illustration shows a schematic diagram of a computer-readable storage medium according to an exemplary embodiment of the present disclosure. Detailed Implementation

[0059] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted.

[0060] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced without one or more of the specific details described, or other methods, components, materials, apparatuses, steps, etc., can be employed. In other instances, well-known structures, methods, apparatuses, implementations, materials, or operations are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0061] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, or in one or more software-hardened modules, or in different network and / or processor devices and / or microcontroller devices.

[0062] In this example embodiment, a fleet consistency formation control method based on a dynamic event triggering mechanism is first provided; see reference Figure 1 As shown, the fleet consistency formation control method based on a dynamic event triggering mechanism may include the following steps:

[0063] Step S110: Based on graph theory, generate a communication topology graph of a multi-agent system composed of a preset number of unmanned vehicles, and build a mathematical model for each unmanned vehicle.

[0064] Step S120: Based on the mathematical model, design the combined measurement function, measurement error and consistency controller of the multi-agent system;

[0065] Step S130: Design distributed event triggering conditions and self-triggering conditions based on Lyapunov parameters;

[0066] Step S140: Calculate the trigger time interval expression of the agent based on the dynamic event triggering condition and the self-triggering condition;

[0067] Step S150: By constructing a multi-agent system consistency controller and combining distributed event triggering conditions and self-triggering conditions, multi-vehicle platooning consistency control is completed.

[0068] This disclosure discloses an exemplary embodiment of a vehicle fleet consistency formation control method based on a dynamic event triggering mechanism. The method includes: generating a communication topology graph of a multi-agent system composed of a preset number of unmanned vehicles (UAVs) based on graph theory, and constructing a mathematical model for each UAV; designing a combined measurement function, measurement error, and consistency controller for the multi-agent system based on the mathematical model; designing distributed event triggering conditions and self-triggering conditions based on Lyapunov parameters; calculating the triggering time interval expression for each agent according to the dynamic event triggering conditions and self-triggering conditions; and completing multi-vehicle fleet consistency control by constructing a multi-agent system consistency controller and combining the distributed event triggering conditions and self-triggering conditions. This disclosure proposes a dynamic event triggering strategy requiring continuous communication and a self-triggering strategy avoiding continuous communication; both control strategies effectively avoid Zeno behavior. In the designed control strategies, individuals only actively acquire neighbor information and update the controller when they trigger themselves, thus reducing the number of controller updates. In the triggering function design process, both fleet error and state estimation error are considered, making the proof process more rigorous and improving the convergence speed of the state observer and the fleet accuracy.

[0069] The following will further explain a fleet consistency formation control method based on a dynamic event triggering mechanism in this example embodiment.

[0070] Example 1:

[0071] In step S110, based on graph theory, a communication topology graph of a multi-agent system composed of a preset number of unmanned vehicles can be generated, and a mathematical model of each unmanned vehicle can be built.

[0072] In this example embodiment, the method further includes:

[0073] Based on graph theory, a communication topology graph of a multi-agent system composed of a preset number of unmanned vehicles is generated according to the speed, heading angle and communication status of each vehicle in the multi-unmanned vehicle system. Then, the adjacency matrix and Laplace matrix are calculated through the topology graph to build a mathematical model of each unmanned vehicle.

[0074] In this example embodiment, the method further includes:

[0075] The multi-agent system, consisting of a preset number of unmanned vehicles, comprises one lead vehicle and multiple follower vehicles. The adjacency matrix and Laplace matrix are calculated using a topology graph to build a dynamic model for each unmanned vehicle.

[0076] In step S120, based on the mathematical model, a combined measurement function, measurement error and consistency controller for the multi-agent system can be designed.

[0077] In this example embodiment, the method further includes:

[0078] Based on the mathematical model, a combined measurement function for the multi-agent system is designed, and an event-triggered measurement error and a distributed multi-vehicle platooning consistency controller based on the event-triggered mechanism are designed based on the combined measurement function.

[0079] In step S130, distributed event triggering conditions and self-triggering conditions can be designed based on Lyapunov parameters.

[0080] In this example embodiment, the method further includes:

[0081] Based on Lyapunov parametric design, a distributed dynamic event-triggered formation control strategy and a distributed self-triggered formation control strategy are proposed.

[0082] In step S140, the triggering time interval expression of the agent can be calculated based on the dynamic event triggering condition and the self-triggering condition.

[0083] In this example embodiment, the method further includes:

[0084] The initialization event triggers the consistency controller, which obtains the initial value of the cooperative state information of adjacent vehicles through the topology communication network;

[0085] The estimated values ​​of the motion state information of adjacent vehicles are obtained by substituting the initial values ​​of the vehicle cooperative state information into the state observer calculation.

[0086] In step S150, a multi-agent system consistency controller can be constructed, which combines distributed event triggering conditions and self-triggering conditions to achieve multi-vehicle platoon consistency control.

[0087] In this example embodiment, the method further includes:

[0088] The trigger function value is calculated based on the estimated motion state of the following vehicle and the motion state information of the lead vehicle. It is then determined whether the communication trigger condition is met. If it is met, a communication is performed to obtain the cooperative status information of the nearby vehicle; otherwise, no communication is performed.

[0089] Each vehicle uses the cooperative state information to input the state observer to obtain the motion state estimates of the leader and followers, calculates the formation consistency control sequence, and transmits it to the vehicle motion controller for execution.

[0090] Example 2:

[0091] In this example embodiment, this disclosure proposes a dynamic event-triggered time-varying formation control method for multi-agent systems based on a state observer. Compared to traditional methods of multi-agent consensus formation control, this scheme proposes a dynamic event-triggered strategy requiring continuous communication and a self-triggered strategy that avoids continuous communication. Both control strategies can effectively avoid Zeno behavior. In the two designed control strategies, individuals only actively acquire neighbor information and update the controller when they trigger their own actions, thus reducing the number of controller updates. In the design of the trigger function, both formation error and state estimation error are considered, making the proof process more rigorous and improving the convergence speed of the state observer and the formation accuracy.

[0092] Specifically, the following steps are included:

[0093] Step 1: Establish a communication network among N autonomous vehicles. This network topology is an undirected graph and satisfies the fully connected condition. Its communication topology is represented by a Laplacian matrix. It means that N ≥ 2;

[0094] Step 2: Establish a dynamic model for the multi-agent system consisting of one lead vehicle and multiple follower vehicles;

[0095] The navigator dynamics model is shown below:

[0096]

[0097] Where x0(t)∈R n , y0(t)∈R q Let f(t, x0(t)) represent the navigator's state, input, and output, respectively, and let f(t, x0(t)) be a nonlinear term or external disturbance.

[0098] The follower dynamics model is shown below:

[0099]

[0100] Where x i (t)∈R n , y i (t)∈R q The states, inputs, and outputs of the navigator are f(t, x), respectively. i (t) represents a nonlinear term or external disturbance.

[0101] A, B, and C are constant matrices, and matrix C is of full row rank. (A,B) is completely controllable and (C,A) is completely observable.

[0102] Step 3: To facilitate the subsequent design of event-driven strategies, define the following combined measurement variable p. i (t);

[0103]

[0104] Where i = 1, 2, ..., N, and Let i and j represent the estimated states of agents i and j, respectively. The state observer has the following form:

[0105]

[0106] Inspired by existing time-varying formation control protocols, the following improved formation control strategy is proposed:

[0107]

[0108] Where α>0 is the gain coefficient, K is the feedback gain matrix, and v i (t) is used to expand the feasible formation set.

[0109] Combined measurement error is defined as:

[0110]

[0111] The state estimation error is defined as:

[0112]

[0113] The output estimation error is defined as:

[0114]

[0115] The following formula is the triggering condition, which can be used to determine the event triggering sequence.

[0116] f1(P i (t),e i (t))≥0,f2(p i (t),ε i (t))≥0 (9)

[0117] In summary, the event triggering control strategy designed in this method satisfies f1(q) i (t),e i (t))<0 and f2(q) i (t), ε i When f1(q) < 0, no event is triggered and the controller is not updated; if f1(q) < 0, no event is triggered and the controller is not updated. i (t),e i (t))≥0 or f2(q) i (t), ε iIf (t))≥0 is satisfied, the event will be triggered, and the agent will actively obtain the state information of the neighboring agents and refresh the controller.

[0118] Step 4: Design a distributed dynamic event-triggered formation control strategy;

[0119] The derivative of the state estimation error is shown below:

[0120]

[0121] It can be seen that formation error does not affect state estimation error. Among other things, By choosing a suitable G matrix such that A-CG is a Hurwitz matrix, we can obtain:

[0122] Since the vehicle communication topology is fully connected, and (A,B) is completely controllable and (C,A) is completely observable, there exists a matrix P>0 satisfying the following inequality:

[0123] PA+A T P-αβPBB T P+αγI n ≤0 (11)

[0124] Where 0 < β < 2λ², γ ≥ λ N And K = B T p. Define the event trigger function shown below. The event will be triggered as long as any one of the trigger conditions is greater than or equal to zero.

[0125]

[0126] in, δ=2||PBB T P||,

[0127] 0 < τ < 0.5,

[0128] 0 < σ < 0.5 and m1 + m2 = 1.

[0129] Step 5: Design a distributed self-triggered grouping control strategy;

[0130] The distributed event triggering mechanism designed in the previous step requires continuous communication between agents to calculate the trigger time, increasing the system's communication burden. Based on the optimization of the previous step, this step designs a distributed self-triggering strategy that avoids continuous communication. The improved control strategy only uses agent i's own trigger time. The collected information does not require obtaining neighbor information between the two triggering times. Define the following self-triggering function:

[0131]

[0132] in,

[0133] and satisfy:

[0134] Where κ>0, the event will be triggered as long as any one of the conditions is met.

[0135] Step 6: Initialize the event-triggered consistency controller and obtain the initial values ​​of the cooperative state information of adjacent vehicles through the topology communication network;

[0136] Step 7: Substitute the initial value of the vehicle cooperative state information into formula (4) to calculate the estimated value of the motion state information of adjacent vehicles, where the state vector in (4) is... This represents the estimated value of the cooperative state vector between two time points;

[0137] Step 8: Based on the estimated motion state of the nearby following vehicle and the motion state information of the leading vehicle, substitute the values ​​into formulas (12) and (13) to calculate the trigger function value, and determine whether the communication trigger condition is met. If it is met, then perform a communication to obtain the cooperative state information of the nearby vehicle; otherwise, do not communicate.

[0138] Step 9: Each vehicle, based on the latest cooperative state information, substitutes it into formula (4) to obtain the estimated motion state values ​​of the leader and followers, calculates the formation consistency control sequence through formula (5), and transmits it to the vehicle motion controller for execution. Steps 8 and 9 are executed repeatedly to finally achieve unmanned vehicle formation consistency control.

[0139] In this example embodiment, this disclosure is applicable to large-scale agent formation consensus control applications. This scheme adopts a distributed control architecture, treating all agents within the system as having equal control priority, and eliminates the need for a central control node, providing a theoretical foundation for the construction of distributed architectures for multi-agent system control. This disclosure studies the time-varying formation control problem of multi-agent systems with undirected communication topology, proposing a distributed dynamic event-triggered and self-triggered control strategy based on observers. In the design of the trigger function, both formation error and state estimation error are considered, improving the accuracy of formation control. This disclosure employs a distributed dynamic event-triggered strategy, which can achieve asymptotic formation and avoid Zeno behavior. A self-triggered control strategy is further designed, where each individual uses the current moment to obtain information about itself and its neighbors, updating the next trigger moment, thereby avoiding continuous communication, reducing the number of communications, and also avoiding Zeno behavior. Finally, simulation results demonstrate the effectiveness of the proposed event-triggered control strategy in multi-agent formation consensus control.

[0140] Example 3:

[0141] In this example embodiment, an intelligent unmanned vehicle consistency formation control method based on a dynamic event-triggered control mechanism includes the following steps:

[0142] Step S110: Based on the theoretical foundation of graph theory, the communication relationship of the multi-agent system composed of three unmanned vehicles is described according to the speed, heading angle and communication status of each vehicle in the multi-unmanned vehicle system, and the communication topology graph of the system is obtained. Then, the adjacency matrix and Laplace matrix are calculated through the topology graph to build the mathematical model of each unmanned vehicle.

[0143] Specifically as follows: Let... It is an undirected graph generated by the inter-vehicle communication network, in which Let G be the set of vertices or nodes of the graph (each vertex corresponds to a vehicle), and ε be the set of edges (each edge represents a data link). A graph is called connected if there is a path connecting any two nodes. Let N be the set of nodes. [ [i] is the set of neighboring nodes that communicate with node i. (Figure) The adjacency matrix, denoted as A, is a square matrix obtained by the row and column indices of the nodes, denoted as A = [a ij ] N×N If j∈N i Then a ij =1, otherwise 0. The in-degree matrix D of the graph is a diagonal matrix, denoted as D = diag{d in (i)}, Let be the in-degree of node i. The Laplacian matrix is ​​a matrix describing the relationship between nodes and edges in a graph, denoted as L = DA. The elements of matrix L satisfy:

[0144]

[0145] From the expression, we can deduce that the row sum of matrix L is equal to zero, meaning that zero is an eigenvalue of matrix L, and 1 is the corresponding right eigenvector. Specifically, in an undirected graph, since a... ij =a ji Therefore, the Laplace matrix L of an undirected graph is a symmetric matrix.

[0146] In this embodiment, a multi-intelligent vehicle fleet system consisting of three autonomous vehicles is considered. A corresponding communication topology is established based on each vehicle's speed, heading angle, and communication status, such as... Figure 2 As shown, the lead-follow model consists of one lead vehicle and two follower vehicles.

[0147] v L With v R These are the speeds of the driverless car's left and right front wheels, v. c Let be the velocity of the autonomous vehicle along its forward direction, d be the distance between the two wheels of the autonomous vehicle, and θ be the angle between the forward direction of the autonomous vehicle and the X-axis of the ground coordinate system. Define the angular velocity w of the autonomous vehicle. c And there are The following dynamic model can be obtained:

[0148]

[0149] v c v L With v R Defined in the body coordinate system, θ and w c Defined in a ground coordinate system, variables in the two coordinate systems can be transformed using the following rotation matrix R:

[0150]

[0151] Define the positions of the autonomous vehicle in the ground coordinate system as x and y, and obtain the forward motion model and inverse kinematics model of the autonomous vehicle:

[0152]

[0153] Step S120: Design the combined measurement function of the multi-agent system, and design the event-triggered measurement error and the distributed multi-vehicle platooning consistency controller based on the combined measurement function;

[0154] Specifically, in this example, the formation is designed as h1 = [0.3, 0.3]. T h2 = [0.3, -0.3] T Define formation errors σ1(t) and σ2(t), and tracking error. From the communication topology of the navigator and the follower, we can obtain Define combined measurement The controller parameters are α = 6, β = 0.5, p = 0.3, T = 0.001s, and λ = 0.5.

[0155] The controller is shown below:

[0156]

[0157] The measurement error takes the form shown below:

[0158]

[0159] Step S130: Design distributed event triggering conditions and self-triggering conditions based on parametric Lyapunov;

[0160] The dynamic event triggering function takes the form shown below:

[0161]

[0162] The self-triggered function takes the form shown below:

[0163]

[0164] Step S140: Calculate the triggering time interval expression of the agent based on the dynamic event triggering condition and the self-triggering condition;

[0165] Specifically as follows:

[0166] This example first designs a dynamic event-triggered strategy to implement multi-vehicle platooning control, requiring neighbors to communicate at each sampling time. This places a significant burden on the system's communication network. The triggering intervals for the autonomous vehicles are shown below:

[0167]

[0168] Secondly, a self-triggering strategy based on sampled data was designed, with each agent using only... The data collected at each time point is used to predict the trigger time, and neighbor information needs to be collected only at the trigger time. Here, β>0 is the trigger parameter and satisfies te[mT,mT+T], if and only if H is satisfied. i The event is triggered when (t)≥0. The agent only broadcasts its own information when it receives a request. The self-triggering time interval of the autonomous vehicle is as follows:

[0169]

[0170] Step S150: By constructing a multi-agent system consensus controller and combining distributed event triggering conditions and self-triggering conditions, complete the simulation and experimental verification of multi-vehicle platoon consensus control.

[0171] Specifically, the following steps were taken: First, the designed dynamic event triggering strategy and self-determination strategy were respectively substituted into the unmanned vehicle platooning model to complete the consistency control of the multi-unmanned vehicle system, thus solving the unmanned vehicle platooning control problem.

[0172] A multi-vehicle system consisting of three autonomous vehicles was simulated in the Simulink environment. Each vehicle has perception and communication capabilities. Under the given conditions, the method proposed in this disclosure was simulated and verified, and the simulation results were analyzed. Figure 3 This is a block diagram of a finite-time vehicle platooning control system based on an event-triggered mechanism.

[0173] First, an experiment was conducted using a finite-time formation tracking control strategy, which involves periodically updating the controller of each autonomous vehicle without incorporating event-triggered control. Figure 4 and Figure 5 The tracking error under this strategy is shown respectively. The curve showing the change of qi(t) in combination with the measurement results. It can be seen that... The function qi(t) gradually converges over time and eventually converges into a bounded domain. Figure 6 The image shows the movement trajectories of the virtual navigator and two followers in a plane. The virtual navigator moves in uniform circular motion around the center, while the two follower vehicles maintain a fixed formation with the navigator and also move in circular motion.

[0174] Then, based on the finite-time control strategy, an event-triggered strategy is added, in which the controller of the autonomous vehicle is updated only when an event is triggered. Figure 7 and Figure 8 The tracking error under this strategy is shown respectively. The curve showing the change of q(t) in combination measurement. The expression qi(t) gradually converges to a bounded domain over time. Figure 4 and Figure 5 The comparison shows that the convergence speed is slower because it introduces an event-triggered mechanism, and the controller update frequency is lower than that of the control strategy mentioned above. Figure 9 The trajectory of the lead vehicle and the two following vehicles in the plane.

[0175] Finally, based on the finite-time control strategy, a self-triggered strategy is added, in which the controller of the autonomous vehicle is updated only when an event is triggered. Figure 10 and Figure 11 The tracking error under this strategy is shown respectively. The curve of the change of qi(t) by combination measurement The expression qi(t) gradually converges to a bounded domain over time. Figure 4 and Figure 5 The comparison shows that the convergence speed is slower, but compared to... Figure 7 and Figure 8 It converges relatively quickly. Because it adds a self-triggering mechanism, the controller frequency update falls between the two control strategies mentioned above. Figure 12 The trajectory of the lead vehicle and the two following vehicles in the plane. Figures 13-15 The diagrams show the controller triggering times for each vehicle under the traditional event-triggered strategy, the dynamic event-triggered strategy, and the self-triggered strategy. The analysis leads to the following conclusion: compared to static and periodic event triggering, the threshold in the dynamic event-triggered mechanism changes with time and system state, achieving a balance between reducing the number of event triggers and improving convergence speed, rather than simply sacrificing system performance to reduce the number of triggers.

[0176] It should be noted that although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0177] Furthermore, in this example embodiment, a fleet consistency formation control device based on a dynamic event triggering mechanism is also provided. (See reference...) Figure 16 As shown, the fleet consistency formation control device 200 based on a dynamic event triggering mechanism may include: a mathematical modeling module 210, a measurement function design module 220, a triggering condition design module 230, an interval expression calculation module 240, and a consistency control module 250. Wherein:

[0178] The mathematical modeling module 210 is used to generate a communication topology diagram of a multi-agent system composed of a preset number of unmanned vehicles based on graph theory, and to build a mathematical model for each unmanned vehicle.

[0179] The measurement function design module 220 is used to design the combined measurement function, measurement error and consistency controller of the multi-agent system based on the mathematical model.

[0180] Trigger condition design module 230 is used to design distributed event trigger conditions and self-trigger conditions based on Lyapunov parameters;

[0181] The interval expression calculation module 240 is used to calculate the trigger time interval expression of the agent based on the dynamic event triggering conditions and the self-triggering conditions.

[0182] The consistency control module 250 is used to complete the consistency control of multi-vehicle platooning by constructing a multi-agent system consistency controller and combining distributed event triggering conditions and self-triggering conditions.

[0183] The specific details of each of the above-mentioned vehicle formation consistency control device modules based on dynamic event triggering mechanism have been described in detail in the corresponding vehicle formation consistency control method based on dynamic event triggering mechanism, so they will not be repeated here.

[0184] It should be noted that although several modules or units of a fleet consistency formation control device 200 based on a dynamic event triggering mechanism have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0185] Furthermore, in an exemplary embodiment of this disclosure, an electronic device capable of implementing the above-described method is also provided.

[0186] Those skilled in the art will understand that various aspects of the present invention can be implemented as systems, methods, or program products. Therefore, various aspects of the present invention can be specifically implemented as entirely hardware embodiments, entirely software embodiments (including firmware, microcode, etc.), or embodiments combining hardware and software aspects, collectively referred to herein as “circuit,” “module,” or “system.”

[0187] The following reference Figure 17 To describe an electronic device 500 according to such an embodiment of the present invention. Figure 5 The electronic device 500 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0188] like Figure 17 As shown, the electronic device 500 is manifested in the form of a general-purpose computing device. The components of the electronic device 500 may include, but are not limited to: at least one processing unit 510, at least one storage unit 520, a bus 530 connecting different system components (including storage unit 520 and processing unit 510), and a display unit 540.

[0189] The storage unit stores program code that can be executed by the processing unit 510, causing the processing unit 510 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 510 can perform actions such as...Figure 1 Steps S110 to S150 are shown in the diagram.

[0190] Storage unit 520 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 5201 and / or cache memory 5202, and may further include a read-only memory (ROM) 5203.

[0191] Storage unit 520 may also include a program / utility 5204 having a set (at least one) program module 5205, such program module 5205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0192] Bus 530 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0193] Electronic device 500 can also communicate with one or more external devices 570 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 500, and / or with any device that enables electronic device 500 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 550. Furthermore, electronic device 500 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 560. As shown, network adapter 560 communicates with other modules of electronic device 500 via bus 530. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0194] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0195] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible embodiments, various aspects of the invention may also be implemented as a program product comprising program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of the invention described in the "Exemplary Methods" section above.

[0196] refer to Figure 18 As shown, a program product 600 for implementing the above-described method according to an embodiment of the present invention is described. It may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, the readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0197] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0198] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0199] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0200] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0201] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0202] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

[0203] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A fleet consistency formation control method based on a dynamic event triggering mechanism, characterized in that, The method includes: Based on graph theory, a communication topology graph of a multi-agent system composed of a predetermined number of unmanned vehicles is generated, and a mathematical model of each unmanned vehicle is built. Based on the mathematical model, design the combined measurement function, measurement error and consistency controller for the multi-agent system; Design of distributed event triggering conditions and self-triggering conditions based on Lyapunov parametric design; Based on the dynamic event triggering conditions and self-triggering conditions, the triggering time interval expression of the agent is calculated; By constructing a multi-agent system consistency controller and combining distributed event triggering conditions and self-triggering conditions, multi-vehicle platooning consistency control is achieved.

2. The method as described in claim 1, characterized in that, The method further includes: Based on graph theory, a communication topology graph of a multi-agent system composed of a preset number of unmanned vehicles is generated according to the speed, heading angle and communication status of each vehicle in the multi-unmanned vehicle system. Then, the adjacency matrix and Laplace matrix are calculated through the topology graph to build a mathematical model of each unmanned vehicle.

3. The method as described in claim 2, characterized in that, The method further includes: The multi-agent system, consisting of a preset number of unmanned vehicles, comprises one lead vehicle and multiple follower vehicles. The adjacency matrix and Laplace matrix are calculated using a topology graph to build a dynamic model for each unmanned vehicle.

4. The method as described in claim 1, characterized in that, The method further includes: Based on the mathematical model, a combined measurement function for the multi-agent system is designed, and an event-triggered measurement error and a distributed multi-vehicle platooning consistency controller based on the event-triggered mechanism are designed based on the combined measurement function.

5. The method as described in claim 1, characterized in that, The method further includes: Based on Lyapunov parametric design, a distributed dynamic event-triggered formation control strategy and a distributed self-triggered formation control strategy are proposed.

6. The method as described in claim 1, characterized in that, The method further includes: The initialization event triggers the consistency controller, which obtains the initial value of the cooperative state information of adjacent vehicles through the topology communication network; The estimated values ​​of the motion state information of adjacent vehicles are obtained by substituting the initial values ​​of the vehicle cooperative state information into the state observer calculation.

7. The method as described in claim 3, characterized in that, The method further includes: The trigger function value is calculated based on the estimated motion state of the following vehicle and the motion state information of the lead vehicle. It is then determined whether the communication trigger condition is met. If it is met, a communication is performed to obtain the cooperative status information of the nearby vehicle; otherwise, no communication is performed. Each vehicle uses the cooperative state information to input the state observer to obtain the motion state estimates of the leader and followers, calculates the formation consistency control sequence, and transmits it to the vehicle motion controller for execution.

8. A fleet consistency formation control device based on a dynamic event triggering mechanism, characterized in that, The device includes: The mathematical modeling module is used to generate a communication topology diagram of a multi-agent system composed of a preset number of unmanned vehicles, based on graph theory, and to build a mathematical model for each unmanned vehicle. The measurement function design module is used to design the combined measurement function, measurement error and consistency controller of the multi-agent system based on the mathematical model. The trigger condition design module is used to design distributed event trigger conditions and self-trigger conditions based on Lyapunov parameters. The interval expression calculation module is used to calculate the trigger time interval expression of the agent based on the dynamic event triggering conditions and self-triggering conditions. The consistency control module is used to construct a multi-agent system consistency controller and combine distributed event triggering conditions and self-triggering conditions to complete the consistency control of multi-vehicle platooning.

9. An electronic device, characterized in that, include Processor; and A memory storing computer-readable instructions that, when executed by the processor, implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the method according to any one of claims 1 to 7.

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