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CN-121979281-A - Cooperative control method for multi-unmanned vehicle system

CN121979281ACN 121979281 ACN121979281 ACN 121979281ACN-121979281-A

Abstract

The invention aims to provide a cooperative control method for a multi-unmanned vehicle system, which comprises the steps of constructing a normal differential-partial differential coupling dynamics model of a multi-unmanned vehicle formation, abstracting the multi-unmanned vehicle system into a fractional order normal differential partial differential coupling multi-time-lag multi-intelligent body system, setting a control target of the multi-intelligent body system as a controller based on neighbor information, converging consistency errors of all intelligent bodies i to zero for any initial condition, constructing an enhanced fractional order stability primer, quantitatively limiting convergence rate of the fractional order Chang Weifen partial differential coupling multi-time-lag multi-intelligent body system containing mixed time lags, and utilizing a dual-mode self-adaptive event trigger controller to cooperatively optimize control performance and communication resources. The method disclosed by the disclosure is more widely applicable than the existing method only aiming at integer order, pure ordinary differential or single time lag.

Inventors

  • XING XIAOFEI
  • LIU ZHI

Assignees

  • 广东工业大学
  • 人工智能与数字经济广东省实验室(广州)

Dates

Publication Date
20260505
Application Date
20260209

Claims (10)

  1. 1. A cooperative control method for a multi-unmanned vehicle system is characterized by comprising the following steps: Constructing a normal differential-partial differential coupling dynamics model of multi-unmanned vehicle formation, wherein the normal differential part and the partial differential part in the normal differential-partial differential coupling dynamics model interact through boundary conditions or internal coupling items to describe the interaction between the mass center motion of the unmanned vehicle and the physical field of the vehicle body space; Abstracting the multi-unmanned vehicle system into a fractional order ordinary differential partial differential coupling multi-time-lag multi-agent system; Describing the information communication process between any two intelligent agents in the fractional order Chang Weifen partial differential coupling multi-time-lag multi-intelligent agent system through a directed graph, and setting a control target of the multi-intelligent agent system as a controller based on neighbor information, wherein for any initial condition, the consistency error of all intelligent agents i is converged to zero; By establishing a fractional differential inequality of a continuous positive definite function and parameter constraint conditions, the association of a convergence index and system parameters is clarified, an enhanced fractional stability primer is constructed, and quantitative limitation on the convergence rate of a fractional Chang Weifen partial differential coupling multi-time-lag multi-agent system containing mixed time lags is realized; And the dual-mode self-adaptive event trigger controller is utilized to cooperatively optimize control performance and communication resources.
  2. 2. The cooperative control method for a multi-vehicle-oriented system according to claim 1, wherein the ordinary differential-partial differential coupling dynamics model of the multi-vehicle formation includes: ; ; Wherein, the Respectively representing the position, the speed and the acceleration of the ith vehicle, wherein m is the mass of the vehicle body; Pneumatic resistance coefficient and viscous friction coefficient respectively; Is a control force acting on the translation system; is the associated control gain; Representing a one-dimensional spatial coordinate along the body A distributed temperature field; Respectively the density, specific heat capacity, thermal conductivity and emissivity of the material; is the convective heat transfer coefficient; Is a Stefan-Boltzmann constant; Ambient temperature and far field temperature, respectively; is a thermal conduction delay; for control inputs acting on the temperature profile; the boundary conditions are adiabatic conditions: 。
  3. 3. the cooperative control method for a multi-unmanned vehicle system according to claim 1, wherein: the expression of the fractional order ordinary differential-partial differential coupling multi-time-lag universal model is as follows: ; Wherein, the Representing the fractional order of the system; Is the ODE state vector for agent i; Is PDE state vector for agent i, w is defined in interval Spatial variation on; the representation starts from A carport fractional derivative operator; And A function describing system non-linear dynamics; is a known constant coefficient system matrix in which Is a symmetric positive definite matrix; is ODE discrete time lag; Is a normally differential distribution time lag; Is the intensity coefficient of PDE infinite distribution time lag; is a kernel function of infinitely distributed time lags; Is a control input acting on the ordinary differential part of the agent i; Is a control input acting on the partial derivative part of the intelligent body i; is an initial function of system state; 。
  4. 4. The cooperative control method for a multi-unmanned vehicle system according to claim 1, wherein the describing the information communication process between any two intelligent agents in the fractional order Chang Weifen partial differential coupling multi-time-lag multi-intelligent agent system by using a directed graph, and setting a control target of the multi-intelligent agent system as a controller based on neighbor information, wherein for any initial condition, the consistency error of all intelligent agents i converges to zero, comprises: Information exchange between any 2 of the agents is via a directed graph To describe, wherein a node set Edge set ; If the agent j communicates with the agent i Corresponding adjacency matrix element Directed graph Laplacian matrix of (2) Is defined as And is also provided with ; Defining the average status of all agents And The consistency error of agent i is defined as: ; designing a controller based on neighbor information So that for any initial condition, all consistency errors of agent i Converging to zero at time t, wherein N is the total number of the intelligent agents; 、 The states are respectively the ordinary differential and partial differential states of the intelligent agent i; 、 The sum of the ordinary differential and partial differential states of all the agents is respectively obtained.
  5. 5. The cooperative control method for a multi-vehicle-oriented system according to claim 1, wherein the enhanced fractional order stability primer includes: Is provided with Is a continuous positive definite function if there is a normal number So that for all The following fractional order differential inequality is satisfied: And the parameters satisfy the conditions: ; the acquisition of the attenuation estimate is expressed as: ; Wherein, the Is a single parameter Mitighrenheit function; is in an initial state; Is a thermal conduction delay; ; convergence index Is the only positive root of the following transcendental equation: 。
  6. 6. The method for collaborative control for a multi-unmanned vehicle system according to claim 1, wherein the dual-mode adaptive event trigger controller is expressed as: ; Wherein, the Is a time-varying adaptive control gain for the ordinary differential and partial differential subsystems, respectively; Is based on the cooperative error of the neighbor ordinary differential state; Is based on the cooperative error of the partial differential state of the neighbors; The event trigger time sequences of the ordinary differential and partial differential controllers of agent i, respectively.
  7. 7. The cooperative control method for a multi-unmanned vehicle system according to claim 6, wherein: The self-adaptive gain update law of the dual-mode self-adaptive event trigger controller is as follows: , , Wherein the method comprises the steps of 、 For the time-varying adaptive control of the gain, 、 Is a steady-state gain that is set to be at a constant level, 、 、 In order to adapt the parameters of the device, 、 Is a consistency error.
  8. 8. The cooperative control method for a multi-unmanned vehicle system according to claim 6, wherein: The event triggering conditions of the dual-mode self-adaptive event triggering controller are as follows: ; ; Wherein, the 、 Is the measurement error; 、 is a triggering parameter; The dynamic variable update law of the event triggering condition satisfies: ; ; Wherein, the 、 Is the dynamic variable decay rate.
  9. 9. The cooperative control method for a multi-unmanned vehicle system according to claim 6, wherein: the cooperative error based on the neighbor ordinary differential state And a cooperative error based on neighbor partial differential states The method comprises the following steps: ; ; Wherein, the Is a neighbor set of agent i; Is an adjacency matrix element; 、 The states are the ordinary differential states of the neighbor agent j and the agent i respectively; 、 and the partial differential states of the neighbor agent j and the agent i respectively.
  10. 10. A cooperative control method for a multi-unmanned vehicle system according to claim 3, wherein: the consistency conditions of the system corresponding to the fractional order ordinary differential-partial differential coupling multi-time-lag universal model are as follows: If there is a constant The following conditions are satisfied: 1) ; 2) ; 3) ; 4) ; Wherein, the In order to be a laplace matrix, Are event triggering and self-adaptive parameters; ; Each matrix block is: , , ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; 。

Description

Cooperative control method for multi-unmanned vehicle system Technical Field The disclosure relates to the technical field of automatic control and cooperative control, in particular to a self-adaptive event triggering consistent control method under the constraint of mixed time lag and communication resources aiming at an unmanned vehicle cluster system coupled by ordinary differential partial differential dynamics. Background With the development of advanced delivery platforms such as high-speed aircrafts, high-speed trains, unmanned vehicles and the like, the cooperative execution of tasks by multi-vehicle (airplane) formation becomes a key for improving the task efficiency and the viability. Taking multi-unmanned vehicle formation as an example, cooperative control faces complex dynamics constraint, wherein the dynamics of strong coupling is characterized in that part of high-speed unmanned vehicles run in an extreme environment, and dynamics of the high-speed unmanned vehicles show the characteristic of constant differential and partial differential tight coupling. On one hand, the mass center translation, the attitude and other integral movements of the vehicle body can be described by a normal differential parameter model (usually described by a normal differential equation), and on the other hand, the physical fields of the vehicle body structure such as heat conduction, aeroelastic vibration, propellant shaking and the like have strong spatial distribution characteristics, and can be accurately described by a partial differential parameter model (usually described by a parabolic or hyperbolic partial differential equation). The two parts are mutually influenced by boundary conditions or internal coupling terms to form a uniform coupling dynamics system. Meanwhile, the method also comprises a complex time lag effect, wherein discrete time lag inevitably exists in inter-vehicle communication links such as distance, relay and processing, distribution time lag exists in links such as vehicle-mounted sensor information fusion and actuator response, and physical processes such as heat conduction have history dependency characteristics and can be modeled as infinite distribution time lag. These mixing lags are incorporated into the system, severely affecting the stability and convergence performance of the synergy. Also included are severe communications and computing resource limitations-unmanned vehicle platforms typically have severe limitations on onboard communications bandwidth, computing power, and energy supply. The traditional periodic continuous communication and control updating strategy has high requirements on communication resources, and is difficult to meet the requirements of long-endurance and high-dynamic collaborative tasks. The existing control method for multi-agent formation is mostly based on pure ordinary differential or pure partial differential models, and the coupling dynamics are difficult to accurately describe. Few studies have been conducted on the operation of coupling systems, and there is a lack of unified handling of mixed time lags and optimization of communication resources. The method is characterized in that the existing stability analysis tool is insufficient, the asymptotic stability can only be proved, the relation between the convergence rate and system parameters (such as fractional order and time lag size) cannot be quantitatively given, the relation is very important for evaluating the transient performance and robustness of the unmanned vehicle formation, the control strategy lacks resource consciousness, the traditional event trigger control threshold is fixed, or the self-adaptive control depends on continuous communication, the trigger mechanism and the control law cannot be cooperatively optimized, and the resource is difficult to be saved to the maximum extent while the performance is ensured. Therefore, from the point of solving the specific engineering problem of multi-unmanned vehicle formation coordination, a distributed coordination control method capable of simultaneously processing centralized ordinary differential partial differential dynamics coupling and mixed time lag and intelligently saving communication resources is needed. Disclosure of Invention The disclosure aims to provide a cooperative control method for a multi-unmanned vehicle system, which is used for at least solving one technical problem in the prior art. The technical scheme of the present disclosure is: a cooperative control method for a multi-unmanned vehicle system comprises the following steps: Constructing a normal differential-partial differential coupling dynamics model of multi-unmanned vehicle formation, wherein the normal differential part and the partial differential part in the normal differential-partial differential coupling dynamics model interact through boundary conditions or internal coupling items to describe the interaction between the mass center motion of t