Event-triggered control for containment maneuvering of second-order MIMO multi-agent systems with unmatched uncertainties and disturbances

This paper is concerned with distributed containment maneuvering of second-order Multi-Input Multi-Output (MIMO) multi-agent systems with non-periodic communication and actuation. The agent is subject to unmatched nonlinear dynamics and external disturbances. Event-triggered containment maneuvering control methods is developed based on a modular design. Specifically, an estimator module is constructed based on neural networks and the non-periodic obtained follower information through event-triggered communication. Next, a controller module is designed by using the identified information from the estimator module and a third-order linear tracking differentiator. An event-triggered mechanism is introduced for updating the actuator. Then, a path update law is designed based on the non-periodic leader information through event-triggered communication. The closed-loop system cascaded by the estimation subsystem and control subsystem is proved to be input-to-state stable, and Zeno behavior is excluded in the control process. The proposed method is capable of reducing the consumption of communication and actuation. A simulation example is provided to substantiate the effectiveness of the proposed event-triggered control method for distributed containment maneuvering of second-order MIMO multi-agent systems.     

Consensus based on learning game theory with a UAV rendezvous application

Multi-agent cooperation problems are becoming more and more attractive in both civilian and military applications. In multi-agent cooperation problems, different network topologies will decide different manners of cooperation between agents. A centralized system will directly control the operation of each agent with information flow from a single centre, while in a distributed system, agents operate separately under certain communication protocols. In this paper, a systematic distributed optimization approach will be established based on a learning game algorithm.The convergence of the algorithm will be proven under the game theory framework. Two typical consensus problems will be analyzed with the proposed algorithm. The contributions of this work are threefold. First, the designed algorithm inherits the properties in learning game theory for problem simplification and proof of convergence. Second, the behaviour of learning endows the algorithm with robustness and autonomy. Third, with the proposed algorithm, the consensus problems will be analyzed from a novel perspective.     

Distributed event-triggered adaptive control for second-order nonlinear uncertain multi-agent systems

In this paper, the event-triggered consensus control problem for nonlinear uncertain multi-agent systems subject to unknown parameters and external disturbances is considered. The dynamics of subsystems are second-order with similar structures, and the nodes are connected by undirected graphs. The event-triggered mechanisms are not only utilized in the transmission of information from the controllers to the actuators, and from the sensors to the controllers within each agent, but also in the communication between agents. Based on the adaptive backstepping method, extra estimators are introduced to handle the unknown parameters, and the measurement errors that occur during the event-triggered communication are well handled by designing compensating terms for the control signals. The presented distributed event-triggered adaptive control laws can guarantee the boundness of the consensus tracking errors and the Zeno behavior is avoided. Meanwhile, the update frequency of the controllers and the load of communication burden are vastly reduced. The obtained control protocol is further applied to a multi-input multi-output second-order nonlinear multi-agent system, and the simulation results show the effectiveness and advantages of our proposed method.     

Semi-global leader-following consensus-based formation flight of unmanned aerial vehicles

In this paper, we investigate a formation control problem of multi-agent systems(specifically a group of unmanned aerial vehicles) based on a semi-global leader-following consensus approach with both the leader and the followers subject to input saturation. Utilizing the low gain feedback design technique, a distributed static control protocol and a distributed adaptive control protocol are constructed. The former solves the problem under an assumption that the communication network is undirecte...     

航空结构件智能化加工设备的发展方向

随着工业4.0时代的到来,航空结构件的加工将逐步从数字化时代进入智能化时代,其主要特点是机床软、硬件设备等多智能体系统(Multi-Agent System,MAS)的集成及应用,核心技术在于机床对加工程序的智能化识别,在此基础上实现智能化装夹与基准识别、加工系统实时监控、加工参数的智能化匹配、加工系统智能预警。     

小型核反应堆自主控制及其深空探测应用设想

针对空间小型模块化核反应堆的自主控制问题，提出自主控制体系结构，降低反应堆控制对人的依赖程度，满足了深空探测任务对空间堆自主控制的需求。首先介绍了核反应堆自主控制技术和空间探测自主技术的发展现状，分析了空间小型堆的自主控制需求，然后阐释了自主控制及核反应堆近自主控制的内涵。最后基于空间堆的运行特点，给出小型堆近自主控制分层体系结构的组成元素，并进一步建立了融合决策层和功能层的小型堆多智能体自主控制体系结构。     

Study on the resolution of multi-aircraft flight conflicts based on an IDQN

With the rapid growth of flight flow, the workload of controllers is increasing daily, and handling flight conflicts is the main workload. Therefore, it is necessary to provide more efficient conflict resolution decision-making support for controllers. Due to the limitations of existing methods, they have not been widely used. In this paper, a Deep Reinforcement Learning(DRL) algorithm is proposed to resolve multi-aircraft flight conflict with high solving efficiency. First, the characteristics ...     

Consensus disturbance rejection control of directed multi-agent networks with extended state observer

This paper investigates the consensus disturbance rejection problem among multiple high-order agents with directed graphs. Based on disturbance observers, distributed consensus disturbance rejection protocols are constructed in leaderless and leader-follower consensus setups. Different from the previous related papers, the consensus protocols in this paper are developed in a fully distributed fashion, relying on only the state information of each agent and its neighbors. Sufficient conditions are provided to guarantee that the asymptotic stability of high-order multi-agent systems can be reached with matched disturbances.     

基于深度Q网络的多智能体逃逸算法设计CSCD

当前多智能体追逃博弈问题通常在二维平面下展开研究,且逃逸方智能体运动不受约束,同时传统方法在缺乏准确模型时存在设计控制策略困难的问题。针对三维空间中逃逸方智能体运动受约束的情况,提出了一种基于深度Q网络(DQN)的多智能体逃逸算法。该算法采用分布式学习的方法,逃逸方智能体通过对环境的探索学习得到满足期望的逃逸策略。为提高学习效率,根据任务的难易程度将智能体策略学习划分为两个阶段,并设计了相应的奖励函数引导智能体探索满足期望的逃逸策略。仿真结果表明,该算法所得逃逸策略效果稳定,并且具有泛化能力,在改变一定的初始位置条件后,逃逸方智能体也可成功逃逸。     

基于multi-agent系统的可重构制造单元多视图建模方法

以可重构制造单元为研究对象,以实现其适应生产需求的快速重构为目标,提出了一种建立在multi-agent基础之上的可重构制造单元模型.