Cooperative control method for multi-unmanned vehicle system

Abstract

The invention aims to provide a cooperative control method for a multi-unmanned-vehicle system. The cooperative control method comprises the following steps: constructing an ordinary differential-partial differential coupling dynamic model of a multi-unmanned-vehicle formation; abstracting the multi-unmanned vehicle system into a fractional order ordinary differential and partial differential coupling multi-time-lag multi-agent system; the control target of the multi-agent system is set as follows: for any initial condition based on a controller of neighbor information, the consistency error of all agents i is converged to zero; an enhanced fractional order stability lemma is constructed, and the convergence rate of the fractional order ordinary differential and partial differential coupling multi-time-lag multi-agent system containing the mixed time lag is quantitatively limited; a dual-mode adaptive event trigger controller is utilized to cooperatively optimize control performance and communication resources. The method disclosed by the invention is wider in application range than the existing methods only aiming at integer order, pure ordinary differential or single time delay.

Description

Technical Field

[0001] This disclosure relates to the fields of automatic control and cooperative control technology, and in particular to an adaptive event-triggered consistent control method for an unmanned vehicle swarm system coupled with ordinary differential and partial differential dynamics, under the constraints of mixed time delay and communication resources. Background Technology

[0002] With the development of advanced transportation platforms such as high-speed aircraft, high-speed trains, and unmanned vehicles, multi-vehicle (aircraft) swarms working collaboratively to execute missions have become crucial for improving mission efficiency and survivability. Taking multi-unmanned vehicle swarms as an example, their collaborative control faces complex dynamic constraints, including: strongly coupled dynamic characteristics: some high-speed unmanned vehicles operate in extreme environments, and their dynamics exhibit a close coupling between ordinary differential equations and partial differential equations. On the one hand, the overall motion of the vehicle body, such as the translation of the center of mass and its attitude, can be characterized by ordinary differential parameter models (usually described by ordinary differential equations); on the other hand, the physical fields of the vehicle structure, such as heat conduction, aeroelastic vibration, and propellant sloshing, have strong spatial distribution characteristics and must be accurately described by partial differential parameter models (usually described by parabolic or hyperbolic partial differential equations). These two parts influence each other through boundary conditions or internal coupling terms, forming a unified coupled dynamic system. Furthermore, complex time delay effects are also present: vehicle-to-vehicle communication inevitably suffers from discrete time delays due to distance, relay, and processing; onboard sensor information fusion and actuator response exhibit distributed time delays; and physical processes such as heat conduction inherently possess historical dependence characteristics, which can be modeled as infinite distributed time delays. These mixed time delays coexist in the system, severely impacting the stability and convergence performance of collaborative operations. Additionally, stringent communication and computing resource constraints are present: autonomous vehicle platforms typically impose strict limitations on airborne communication bandwidth, computing power, and energy supply. Traditional periodic continuous communication and control update strategies place high demands on communication resources, making it difficult to meet the requirements of long-endurance, highly dynamic collaborative tasks.

[0003] Existing control methods for multi-agent formations are mostly based on pure ordinary differential or pure partial differential models, which are difficult to accurately characterize the aforementioned coupled dynamics. The few studies on coupled systems lack a unified approach to the optimization of hybrid time delays and communication resources. Specifically, existing stability analysis tools are insufficient, only able to prove asymptotic stability, and unable to quantitatively provide the relationship between convergence rate and system parameters (such as fractional order and time delay magnitude), which is crucial for evaluating the transient performance and robustness of autonomous vehicle formations. Furthermore, control strategies lack resource awareness; traditional event-triggered control has fixed thresholds, or adaptive control relies on continuous communication, failing to coordinate the optimization of triggering mechanisms and control laws, making it difficult to maximize resource conservation while ensuring performance.

[0004] Therefore, starting from solving the specific engineering problem of multi-unmanned vehicle platooning coordination, there is an urgent need for a distributed cooperative control method that can simultaneously handle the coupling of centralized ordinary differential and partial differential dynamics, mixed time delays, and intelligently save communication resources. Summary of the Invention

[0005] The purpose of this disclosure is to provide a cooperative control method for multi-unmanned vehicle systems, in order to solve at least one technical problem in the prior art.

[0006] The technical solution disclosed herein is:

[0007] A cooperative control method for multi-unmanned vehicle systems includes:

[0008] A coupled dynamic model of ordinary differential equations and partial differential equations is constructed for a multi-unmanned vehicle formation; and the ordinary differential and partial differential components in the coupled dynamic model interact with each other through boundary conditions or internal coupling terms to describe the interaction between the motion of the center of mass of the unmanned vehicle and the physical field of the vehicle body space.

[0009] The aforementioned multi-unmanned vehicle system is abstracted as a fractional-order ordinary differential and partial differential coupled multi-time-delay multi-agent system;

[0010] The information communication process between any two agents in a fractional-order ordinary differential and partial differential coupled multi-delay multi-agent system is described by a directed graph; and the control objective of the multi-agent system is set as follows: for any initial conditions, the consistency error of all agents i converges to zero by a controller based on neighbor information.

[0011] By establishing fractional differential inequalities and parameter constraints for continuous positive definite functions, the relationship between the convergence exponent and system parameters is clarified, and an enhanced fractional stability lemma is constructed to quantitatively limit the convergence rate of a fractional ordinary differential and partial differential coupled multi-delay multi-agent system containing mixed time delays.

[0012] By utilizing a dual-mode adaptive event-triggered controller, control performance and communication resources are optimized collaboratively.

[0013] The ordinary differential-partial differential coupled dynamics model of the multi-unmanned vehicle formation includes:

[0014] ;

[0015] ;

[0016] in, Let represent the position, velocity, and acceleration of the i-th vehicle, respectively; m is the mass of the vehicle. These are the aerodynamic drag coefficient and the viscous friction coefficient, respectively. The control force acting on the translational system; For the relevant control gain; Represents a coordinate system along a certain dimension of the vehicle body. The temperature field distribution; These are material density, specific heat capacity, thermal conductivity, and emissivity, respectively. The convective heat transfer coefficient; It is the Stefan-Boltzmann constant; These are the ambient temperature and the far-field temperature, respectively. For the delay of heat conduction; This is the control input that acts on the temperature distribution; The correlation gain is used; the boundary condition is adiabatic. .

[0017] The expression for the fractional-order ordinary differential-partial differential coupled multi-delay general model is as follows:

[0018] ;

[0019] in, Indicates the fractional order of the system; It is the ODE state vector of agent i; Let w be the PDE state vector of agent i; w is defined in the interval Spatial variables on; Indicates the beginning of The Kapto fractional derivative operator; and A function that describes the nonlinear dynamics of a system; It is a known matrix of a system with constant coefficients, where It is a symmetric positive definite matrix; It is an ODE discrete time delay; It is a time delay with ordinary differential distribution; It is the intensity coefficient of the infinite distributed time delay of the PDE; It is a kernel function with infinite distributed time delay; It is the control input that acts on the ordinary differential part of agent i. It is the control input that acts on the partial derivative part of agent i; It is the initial function of the system state; .

[0020] The method describes the information communication process between any two agents in a fractional-order ordinary differential and partial differential coupled multi-delay multi-agent system using directed graphs; and sets the control objective of the multi-agent system as follows: for any initial conditions, the consensus error of all agents i converges to zero, based on the controller's neighbor information, including:

[0021] Information exchange between any two of the aforementioned intelligent agents is mediated by a directed graph. To describe, where the node set edge set ;

[0022] If agent j communicates with agent i, then The corresponding adjacency matrix elements Directed graph Laplace matrix Defined as and ;

[0023] Define the average state of all agents. and Then the consistency error of agent i is defined as:

[0024] ;

[0025] Design a controller based on neighbor information. Such that for any initial conditions, all consistency errors of agent i are... It converges to zero at time t; N is the total number of agents; , These are the ordinary differential and partial differential states of agent i, respectively; , These represent the sum of the ordinary and partial differential states of all agents, respectively.

[0026] The enhanced fractional stability lemma includes:

[0027] set up It is a continuous positive definite function, where If positive numbers exist This makes it possible for all The following fractional differential inequalities must be satisfied:

[0028] And the parameters satisfy the following conditions: ;

[0029] The attenuation estimate is then expressed as:

[0030] ;

[0031] in, It is a single-parameter Mitaglev function; For the delay of heat conduction;

[0032] Convergence index It is the unique positive root of the following transcendental equation:

[0033] .

[0034] The dual-mode adaptive event-triggered controller is represented as follows:

[0035]

[0036] ;

[0037] in, These are time-varying adaptive control gains, used for both ordinary differential and partial differential molecular systems; It is a cooperative error based on the neighbor's ordinary differential state; It is a cooperative error based on the neighbor's partial differential state; These are the event triggering time sequences of the ordinary and partial differential controllers of agent i, respectively.

[0038] The adaptive gain update law of the dual-mode adaptive event-triggered controller is:

[0039] ,

[0040] ,

[0041] in , For time-varying adaptive control gain, , For steady-state gain, , , For adaptive parameters, , This is the consistency error.

[0042] The event triggering conditions for the dual-mode adaptive event-triggered controller are as follows:

[0043] ;

[0044] ;

[0045] in, , For measurement error; , For trigger parameters;

[0046] The dynamic variable update law of the event triggering condition satisfies:

[0047] ;

[0048] ;

[0049] in, , The decay rate is a dynamic variable.

[0050] The cooperative error based on neighbor ordinary differential states and cooperative error based on neighbor partial differential states for:

[0051] ;

[0052] ;

[0053] in, Let i be the set of neighbors of agent i; These are elements of the adjacency matrix; , Let be the ordinary differential states of neighboring agent j and agent i, respectively; , Let be the partial differential states of neighboring agent j and agent i, respectively.

[0054] The consistency condition for the system corresponding to the fractional ordinary differential-partial differential coupled multi-delay general model is as follows:

[0055] If a constant exists This makes the following conditions true:

[0056] 1) ;

[0057] 2) ;

[0058] 3) ;

[0059] 4) .

[0060] The beneficial effects of this disclosure include at least the following:

[0061] This disclosure establishes explicit equations relating the convergence exponent to system parameters and effectively addresses the common problem of mixed time delays in engineering, including the coexistence of discrete, finite-distribution, and infinite-distribution time delays. Furthermore, the proposed dual-mode adaptive event-triggered strategy creatively combines adaptive gain with a dynamic triggering threshold, achieving coordinated optimization of control energy and communication resources. Therefore, the method described in this disclosure has a wider range of applications than existing methods that only address integer-order, purely ordinary differential, or single-delay time delays. Attached Figure Description

[0062] Figure 1 The ordinary differential error components of the three agents The evolutionary trajectory.

[0063] Figure 2 The partial differential error components of the three agents The evolutionary trajectory.

[0064] Figure 3 This chart compares the number of event triggers for the controller under three strategies: dual-modal adaptive event-triggered control, traditional static event-triggered control, and dynamic event-triggered control.

[0065] Figure 4 Synchronization error of the ordinary molecular system of unmanned vehicles.

[0066] Figure 5 The state of the partial molecular system of the unmanned vehicle and its synchronization error. Detailed Implementation

[0067] The present disclosure will now be further explained with reference to the accompanying drawings.

[0068] The purpose of this disclosure is to address the control challenges arising from the coupling of ordinary differential and partial differential dynamics, hybrid time delays, and limited communication resources in the coordinated operation of multiple unmanned vehicles and other transportation vehicles. To this end, this disclosure proposes a novel adaptive event-triggered cooperative control method, with the following specific objectives:

[0069] 1. Establish an ordinary differential and partial differential coupled dynamic model applicable to multi-unmanned vehicle formations to accurately describe the interaction between its overall motion and the space physical field.

[0070] 2. An enhanced convergence analysis lemma is proposed to establish a rigorous convergence criterion for coupled systems containing mixed time delays, and to explicitly quantify the convergence rate, providing a theoretical basis for controller design.

[0071] 3. Design a dual-mode adaptive event-triggered controller that can adjust the control gain and communication trigger threshold in real time according to the system status, while ensuring high-precision consistency of the formation and significantly reducing the frequency of inter-vehicle communication.

[0072] 4. Provide a complete set of controller design, stability proof and parameter selection criteria to ensure the feasibility and reliability of the proposed method in actual systems.

[0073] Based on this, the present disclosure provides the following embodiments. Specific Implementation Example 1:

[0075] This disclosure provides an embodiment:

[0076] To clearly illustrate the core engineering problem addressed by this disclosure, a cooperative control method for multi-unmanned vehicle systems includes the following steps:

[0077] S1: First, we model a specific example using a formation of three (N=3) unmanned vehicles.

[0078] The dynamics of each vehicle involves both rigid body motion and the temperature field distribution of key parts of the vehicle body, which is described by partial differential equations, constituting a typical ordinary differential and partial differential coupled system. The rigid body motion is described by ordinary differential equations; the temperature field distribution of key parts of the vehicle body, such as the nose cone and wing leading edge, is described by partial differential equations. The dynamics of the i-th vehicle are described by the following equations:

[0079] ;

[0080]

[0081] in, Let represent the position, velocity, and acceleration of the i-th vehicle, respectively; m is the mass of the vehicle. These are the aerodynamic drag coefficient and the viscous friction coefficient, respectively. The control force (such as thrust) acting on a translational system; This refers to the relevant control gain. Represents a coordinate system along a certain dimension of the vehicle body. The temperature field distribution; These are material density, specific heat capacity, thermal conductivity, and emissivity, respectively. The convective heat transfer coefficient; It is the Stefan-Boltzmann constant; These are the ambient temperature and the far-field temperature, respectively. For the delay of heat conduction; For control inputs that affect temperature distribution (such as active cooling control); This represents the correlation gain. The boundary condition is adiabatic. .

[0082] To facilitate analysis and controller design, normalized variables are introduced: ,in After normalization, the coupled system can be rewritten in a more compact form:

[0083]

[0084] + ,

[0085] in To normalize the speed, To normalize the temperature distribution, For control input. The nonlinear terms are respectively... and , =40. Assume and The average consistency error is defined as... .

[0086] definition Therefore, the uniform error system is:

[0087] ;

[0088] ;

[0089] Its control objective is design. and This causes all errors to converge asymptotically to zero.

[0090] S2: General theoretical model abstraction: Fractional-order ordinary differential and partial differential coupled multi-delay multi-agent system:

[0091] From the engineering example of the unmanned vehicle platoon constructed in step S1, a more general scientific problem can be abstracted: how to control the consistency problem of a class of multi-agent systems coupled with ordinary differential and partial differential dynamics and affected by mixed time delays. To this end, this embodiment proposes the following general theoretical model.

[0092] Consider a fractional-order ordinary differential and partial differential coupled multi-delay multi-agent system consisting of N agents. Each agent i... The dynamics are described by the following equations:

[0093] ;

[0094] in, This represents the fractional order of the system. When... When this happens, the system degenerates into an integer-order system. It is the ODE state vector of agent i. Let be the PDE state vector of agent i. Let w be the vector defined in the interval . Spatial variables on. Indicates the beginning of The Caputo fractional derivative operator. and A function that describes the nonlinear dynamics of a system. It is a known matrix of a system with constant coefficients, where It is a symmetric positive definite matrix. It is an ODE discrete time delay. It is a time delay of ordinary differential distribution. It is the intensity coefficient of the infinite distributed time delay of the PDE. It is a kernel function with infinite distributed time delay. It is the control input that acts on the ordinary differential part of agent i. It is the control input that acts on the partial differential part of agent i. It is the initial function of the system state. .

[0095] The above model is widely applicable to describing multi-agent systems with coupled ordinary differential and partial differential dynamics, summarizing the core characteristics of many practical systems such as autonomous vehicle platooning: fractional-order dynamics, coupling of ordinary differential and partial differential dynamics, and hybrid time delays. For example, in multi-autonomous vehicle platooning, It can characterize the motion state of the center of mass of the i-th vehicle (such as position and velocity). It can characterize the temperature field or vibration mode distribution of the vehicle body along the axial direction w. The core theoretical task of this disclosure is to address the consistency control problem of this general model. The hybrid time delay includes discrete time delay, finite distributed time delay, and infinite distributed time delay.

[0096] For the purposes of theoretical analysis, the following standard assumptions are introduced:

[0097] There exists a positive definite matrix , so that for any have:

[0098] ,

[0099] ;

[0100] Time-delay kernel function .

[0101] The control framework proposed in this embodiment is directly designed for fractional-order time-delay models, enabling it to describe a wider range of dynamic characteristics. It also handles coupled ordinary and partial differential dynamics, making the model more realistic. Furthermore, it is compatible with mixed time delays of discrete, distributed, and infinitely distributed types, exhibiting stronger robustness to communication, computation, and transmission delays in real-world systems. Therefore, this approach has a broader applicability than existing methods that only address integer-order, purely ordinary differential, or single-delay models.

[0102] S3: Definition of Communication Topology and Consistency Error

[0103] Information exchange between intelligent agents is mediated by a directed graph. To describe, where the node set edge set If agent j communicates with agent i, then... The corresponding adjacency matrix elements .picture Laplace matrix Defined as and This embodiment assumes... It is strongly connected.

[0104] Define the average state of all agents. and The consistency error of agent i is then defined as: .

[0105] The system's control objective is to design a controller based on neighbor information. Such that for any initial conditions, all It converges to zero.

[0106] S4: Core Theoretical Tool: Enhanced Fractional Stability Lemma

[0107] To address the problem of the inability to accurately quantify the convergence rate in the prior art, this disclosure proposes a new fractional stability lemma.

[0108] Lemma 1: Let It is a continuous positive definite function, where If positive numbers exist. This makes it possible for all The following fractional differential inequalities must be satisfied:

[0109] ;

[0110] And the parameters meet the following conditions: ;

[0111] Therefore, there exists a positive constant. This makes the following attenuation estimate hold true:

[0112] .

[0113] in, It is a one-parameter Mitaglev function. Convergence index. It is the unique positive root of the following transcendental equation:

[0114] .

[0115] Lemma 1 provided in this embodiment overcomes the problem of difficulty in quantifying the convergence rate in the stability analysis of existing fractional-order time-delay systems. It establishes explicit equations relating the convergence exponent to the system parameters and effectively addresses the common problem of mixed time delays in engineering, such as the coexistence of discrete time delays, finite-distributed time delays, and infinite-distributed time delays.

[0116] S5: Design of a Dual-Mode Adaptive Event-Triggered Controller

[0117] To address the consistency issues of the aforementioned general models and simultaneously optimize communication resources, this disclosure proposes a unified dual-mode adaptive event-triggered control scheme. The core of the controller in this disclosure is dual-mode adaptation: Mode 1 is adaptive control gain, and Mode 2 is a time-varying event triggering threshold. The controller utilizes the agent's own state information and the triggering time information of its neighbors.

[0118] S501. Controller Structure:

[0119] The controller is in the following form:

[0120] .

[0121] in: It is a time-varying adaptive control gain, used for both ordinary differential and partial differential molecular systems. It is a cooperative error based on the neighbor's ordinary differential state. It is based on the cooperative error of the neighbor's partial differential state. These are the event triggering time sequences of the ordinary and partial differential controllers of agent i, respectively, which are determined by the subsequent triggering mechanism, as shown in formulas (3)-(4).

[0122] S502. Adaptive gain update law, referred to as Mode 1 in this embodiment:

[0123] Control gain The update law is designed to perform online adjustments based on real-time consistency errors:

[0124] ;

[0125] .

[0126] This is the steady-state gain value, a design parameter. It is an adaptive parameter.

[0127] S503. Event triggering mechanism, referred to as Mode 2 in this embodiment:

[0128] To reduce communication, the controller updates only when a specific event occurs. Define the measurement error:

[0129] ;

[0130] The event triggering conditions for agent i are designed as follows:

[0131]

[0132]

[0133] in: and It is the trigger parameter. It is a time-varying internal dynamic variable, forming the core of the dynamic threshold. Its update law is designed as follows:

[0134] ;

[0135] ;

[0136] in, It refers to the decay rate of a dynamic variable. The significance of this mechanism lies in: triggering a threshold. It is time-varying. When measurement error... When the error is small, the triggering conditions will be relaxed and the number of triggers will be reduced; conversely, when the error may increase, the triggering conditions will be tightened and the control signal will be updated in a timely manner to maintain performance.

[0137] The dual-mode adaptive event-triggered strategy proposed in this embodiment creatively combines adaptive gain with dynamic triggering threshold, achieving synergistic optimization of control energy and communication resources. Adaptive gain enhances the robustness of the control system, while the time-varying threshold intelligently adjusts the communication frequency. Simulations show that, under the same consistency accuracy, this strategy reduces the number of communications by 30%-50% compared to traditional static ETC. Furthermore, the controller provided in this embodiment represents a complete design process from theoretical lemmas, control strategies, parameter updates to stability condition proofs, particularly transforming complex stability conditions into numerically solvable linear matrix inequalities, facilitating application and verification by engineers.

[0138] S6: Consistency condition:

[0139] Under the aforementioned controller, update law, and triggering mechanism (see formulas (3)-(4), based on Lyapunov stability theory and the aforementioned lemma 1, the consistency condition for the closed-loop system (see formula (2)) is derived:

[0140] Theorem 1: If there exists a constant This makes the following conditions true:

[0141] 1) ;

[0142] 2) ;

[0143] 3) ;

[0144] 4) ;

[0145] Each matrix block is as follows:

[0146] ;

[0147] ; ; .

[0148] in: .

[0149] Therefore, the closed-loop system (2) can achieve consistency. At the same time, the proposed event triggering mechanism, as shown in formulas (3)-(4), can avoid Zeno behavior.

[0150] S7: Simulation Verification Example

[0151] To verify the effectiveness of the method disclosed herein, two simulations were performed:

[0152] S701. Verification of theoretical validity:

[0153] Consider a system with 3 agents. A fractional-order ordinary differential and partial differential coupled multi-delay multi-agent system was constructed to verify its consistency. The system model follows formula (2) in the main text, and the specific configuration is as follows:

[0154] System parameters: .

[0155] Nonlinear functions: Partially differential molecular systems: ;

[0156] in, .

[0157] Ordinary molecular systems: .

[0158] The Laplace matrix and correlation matrix are as follows:

[0159] .

[0160] The selection of event triggering and controller parameters ensures that all conditions of Theorem 1 are met:

[0161] .

[0162] The simulation results are as follows: Figure 1 shows the ordinary differential state error. The asymptotic convergence to zero indicates that the state of the ordinary molecular system of the intelligent agent has reached a consensus. Figure 2 shows the partial differential state error variable. Convergence to zero proves that the states of the partial differential molecular system of the agents have reached consensus. Figure 3 shows a comparison of the number of event triggers of the controller under three strategies in the simulation example: the bimodal adaptive event-triggered control of this disclosure, the traditional static event-triggered control, and the dynamic event-triggered control. The bimodal adaptive event-triggered control corresponds to fewer triggers, thus saving communication resources of the multi-agent system. Conclusion: Simulation verifies that under the action of the designed adaptive event-triggered controller, the fractional-order ordinary differential and partial differential coupled multi-time-delay multi-agent system achieves consensus.

[0163] S702. Engineering Verification

[0164] Consider a system with 3 agents. The consistency of the unmanned vehicle platoon model is verified. The system model follows formula (1) in the text, and the system physical and control parameters are set as follows:

[0165] The Laplace matrix of the communication topology is , The initial conditions are: Time Delay .

[0166] The selected controller parameters satisfy all the conditions of the theorem in this embodiment, and the specific values ​​are as follows:

[0167] Simulations were performed based on the above parameters. Figure 4 This demonstrates the consistency error of ordinary molecular systems, such as the speed of autonomous vehicles. It converges asymptotically to zero. Figure 5 This demonstrates the state trajectories of partially differential molecular systems, such as temperature distribution. and its consistency error It also converges to zero.

[0168] Simulation results show that even in hypersonic formation systems with complex nonlinear and distributed parameter characteristics, the designed adaptive event-triggered controller can still effectively achieve consistency of the entire unmanned vehicle formation system, verifying the robustness and engineering application potential of the control strategy.

[0169] In summary, this disclosure takes the typical complex system of multiple autonomous vehicles as its background, abstracts a general mathematical dynamic model of a fractional-order ordinary differential and partial differential coupled multi-time-delay multi-agent system, and proposes a complete theoretical framework for a consensus control method based on this model, and conducts detailed simulation verification. The results show that the method can effectively handle the consensus problem of autonomous vehicles under dynamic coupling, mixed communication delays, and resource constraints, providing a directly referable solution for the cooperative control of advanced mobile platforms. Specific Implementation Example 2:

[0171] This disclosure also provides an embodiment:

[0172] An electronic device for collaborative control of multiple unmanned vehicle systems includes: a storage medium and a processing unit; wherein the storage medium is used to store a computer program; the processing unit exchanges data with the storage medium and is used to execute the computer program through the processing unit during collaborative control of multiple unmanned vehicle systems, performing the steps of the collaborative control method for multiple unmanned vehicle systems as described in Specific Embodiment 1.

[0173] A computer-readable storage medium: the computer-readable storage medium stores a computer program;

[0174] When the computer program is running, it executes the steps of the cooperative control method for multi-unmanned vehicle systems as described in Specific Embodiment 1.

[0175] It should be clarified that, in this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wireline, optical fiber, RF, etc., or any suitable combination thereof.

[0176] The above disclosures only cover a few specific implementation scenarios. However, this disclosure is not limited to these, and any variations that can be conceived by those skilled in the art should fall within the protection scope of this disclosure. The serial numbers in this disclosure are for descriptive purposes only and do not represent the superiority or inferiority of the implementation scenarios.

Claims

1. A cooperative control method for multi-unmanned vehicle systems, characterized in that, include: A coupled dynamic model of ordinary differential equations and partial differential equations is constructed for a multi-unmanned vehicle formation; and the ordinary differential and partial differential components in the coupled dynamic model interact with each other through boundary conditions or internal coupling terms to describe the interaction between the motion of the center of mass of the unmanned vehicle and the physical field of the vehicle body space. The aforementioned multi-unmanned vehicle system is abstracted as a fractional-order ordinary differential and partial differential coupled multi-time-delay multi-agent system; The information communication process between any two agents in a fractional-order ordinary differential and partial differential coupled multi-delay multi-agent system is described by directed graphs. The control objective of the multi-agent system is set as follows: for any initial conditions, the consistency error of all agents i converges to zero in a controller based on neighbor information. By establishing fractional differential inequalities and parameter constraints for continuous positive definite functions, the relationship between the convergence exponent and system parameters is clarified, and an enhanced fractional stability lemma is constructed to quantitatively limit the convergence rate of a fractional ordinary differential and partial differential coupled multi-delay multi-agent system containing mixed time delays. By utilizing a dual-mode adaptive event-triggered controller, control performance and communication resources are optimized collaboratively.

2. The cooperative control method for multi-unmanned vehicle systems according to claim 1, characterized in that, The ordinary differential-partial differential coupled dynamics model of the multi-unmanned vehicle formation includes: ； ； in, Let represent the position, velocity, and acceleration of the i-th vehicle, respectively; m is the mass of the vehicle. These are the aerodynamic drag coefficient and the viscous friction coefficient, respectively. The control force acting on the translational system; For the relevant control gain; Represents a coordinate system along a certain dimension of the vehicle body. The temperature field distribution; These are material density, specific heat capacity, thermal conductivity, and emissivity, respectively. The convective heat transfer coefficient; It is the Stefan-Boltzmann constant; These are the ambient temperature and the far-field temperature, respectively. For the delay of heat conduction; This is the control input that acts on the temperature distribution; The correlation gain is used; the boundary condition is adiabatic. .

3. The cooperative control method for multi-unmanned vehicle systems according to claim 1, characterized in that: The expression for the fractional-order ordinary differential-partial differential coupled multi-delay general model is as follows: ； in, Indicates the fractional order of the system; It is the ODE state vector of agent i; Let w be the PDE state vector of agent i; w is defined in the interval Spatial variables on; Indicates the beginning of The Kapto fractional derivative operator; and A function that describes the nonlinear dynamics of a system; It is a known matrix of a system with constant coefficients, where It is a symmetric positive definite matrix; It is an ODE discrete time delay; It is a time delay with ordinary differential distribution; It is the intensity coefficient of the infinite distributed time delay of the PDE; It is a kernel function with infinite distributed time delay; It is the control input acting on the ordinary differential part of agent i; It is the control input that acts on the partial derivative part of agent i; It is the initial function of the system state; .

4. The cooperative control method for multi-unmanned vehicle systems according to claim 1, characterized in that, The method describes the information communication process between any two agents in a fractional-order ordinary differential and partial differential coupled multi-delay multi-agent system using directed graphs; and sets the control objective of the multi-agent system as follows: for any initial conditions, the consensus error of all agents i converges to zero, based on the controller's neighbor information, including: Information exchange between any two of the aforementioned intelligent agents is mediated by a directed graph. To describe, where the node set edge set ; If agent j communicates with agent i, then The corresponding adjacency matrix elements Directed graph Laplace matrix Defined as and ; Define the average state of all agents. and Then the consistency error of agent i is defined as: ; Design a controller based on neighbor information. Such that for any initial conditions, all consistency errors of agent i are... It converges to zero at time t; N is the total number of agents; , These are the ordinary differential and partial differential states of agent i, respectively; , These represent the sum of the ordinary and partial differential states of all agents, respectively.

5. The cooperative control method for multi-unmanned vehicle systems according to claim 1, characterized in that, The enhanced fractional stability lemma includes: set up It is a continuous positive definite function, if it has positive constants. This makes it possible for all The following fractional differential inequalities must be satisfied: And the parameters satisfy the following conditions: ; The attenuation estimate is then expressed as: ； in, It is a single-parameter Mitaglev function; This is the initial state; For thermal conduction delay; ; Convergence index It is the unique positive root of the following transcendental equation: 。 6. The cooperative control method for multi-unmanned vehicle systems according to claim 1, characterized in that: The dual-mode adaptive event-triggered controller is represented as follows: ； in, These are time-varying adaptive control gains, used for both ordinary differential and partial differential molecular systems; It is a cooperative error based on the neighbor's ordinary differential state; It is a cooperative error based on the neighbor's partial differential state; These are the event triggering time sequences of the ordinary and partial differential controllers of agent i, respectively.

7. The cooperative control method for multi-unmanned vehicle systems according to claim 6, characterized in that: The adaptive gain update law of the dual-mode adaptive event-triggered controller is: ， ， in , For time-varying adaptive control gain, , For steady-state gain, , , For adaptive parameters, , This is the consistency error.

8. The cooperative control method for multi-unmanned vehicle systems according to claim 6, characterized in that: The event triggering conditions for the dual-mode adaptive event-triggered controller are as follows: ； ； in, , For measurement error; , For triggering parameters; The dynamic variable update law of the event triggering condition satisfies: ； ； in, , The decay rate is a dynamic variable.

9. The cooperative control method for multi-unmanned vehicle systems according to claim 6, characterized in that: The cooperative error based on neighbor ordinary differential states and cooperative error based on neighbor partial differential states for: ； ； in, Let i be the set of neighbors of agent i; These are elements of the adjacency matrix; , Let be the ordinary differential states of neighboring agent j and agent i, respectively; , Let be the partial differential states of neighboring agent j and agent i, respectively.

10. The cooperative control method for multi-unmanned vehicle systems according to claim 3, characterized in that: The consistency condition for the system corresponding to the fractional ordinary differential-partial differential coupled multi-delay general model is as follows: If a constant exists This makes the following conditions true: 1) ； 2) ； 3) ； 4) ； in, For Laplace matrix, Both are event-triggered and adaptive parameters; ; Each matrix block is as follows: , ， ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； 。

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