基于自适应分布式滤波观测器的多智能体系统编队控制

本文考虑了全局指令系统输出信息受到信道扰动情况下线性多智能体系统的编队控制问题.首先,基于协作式输出调节理论框架对线性多智能体系统的编队控制问题进行数学建模.其次,针对受到信道扰动的全局指令系统输出信息,提出了一类基于受扰输出的自适应分布式滤波观测器,在降低网络信息交换量的同时消除扰动的影响.最后,设计了输出反馈确定等价控制律,解决了线性多智能体系统的分布式编队控制问题.给出了数值仿真结果检验控制性能.     

一种基于分布式强化学习的多智能体协调方法

多智能体系统研究的重点在于使功能独立的智能体通过协商、协调和协作,完成复杂的控制任务或解决复杂的问题。通过对分布式强化学习算法的研究和分析,提出了一种多智能体协调方法,协调级将复杂的系统任务进行分解,协调智能体利用中央强化学习进行子任务的分配,行为级中的任务智能体接受各自的子任务,利用独立强化学习分别选择有效的行为,协作完成系统任务。通过在Robot Soccer仿真比赛中的应用和实验,说明了基于分布式强化学习的多智能体协调方法的效果优于传统的强化学习。     

集合约束下多智能体系统分布式固定时间优化控制

本文研究一类具有状态约束的多智能体系统优化控制问题, 提出了一种具有固定时间收敛特性的分布式优化控制算法. 该控制算法由局部投影模块、一致性模块和梯度模块构成, 其中局部投影模块确保智能体的状态在固定时间内收敛到局部约束集合, 基于时变增益的一致性模块实现所有智能体的状态在固定时间内收敛到一致值, 基于时变增益的梯度模块实现智能体的状态在固定时间内收敛到最优解. 利用凸优化理论和固定时间李雅普诺夫理论, 分析了算法的固定时间收敛特性. 算法收敛时间的上界值不依赖系统的初始条件, 因而可以根据任务需求来预先设计收敛时间. 最后通过数值仿真验证了理论结果的有效性.     

多智能体机器人系统分布式通信决策研究

无偿通信可以将Multi-agent POMDP的计算复杂度简化为单Agent POMDP的，然而实际上通信不是无偿的，常常期望减少通信的数量。为此提出了一个新的方法，利用有向无环图维持以及推理团队的可能联合信度，基于此以分布式的方式制定通信决策，并将集中式单Agent策略应用于分布式Multi-agent POMDP问题。通过实验以及一个详细的实例表明本文方法能够有效地减少通信资源的使用，同时提高分布执行的性能。     

多智能体系统分布式一致性算法研究现状

综述了多智能体系统分布式一致性问题的研究现状。从理论层面介绍了一致性问题的几种常见定义及与特性相关的主要参数;总结归纳了近年来几种一致性协议及其理论分析结果;分析和阐述了一致性问题的主要应用领域的进展。展望了未来的研究方向。     

基于近似模拟的多智能体系统分布式层次控制设计

本文主要探讨基于近似模拟的多智能体系统分布式层次控制的设计问题。为降低问题复杂度,首先建立了一个简单的抽象系统指导各智能体,继而讨论这一抽象系统和多智能体之间的模拟关系。借助于该抽象系统,给出多智能体系统完成一类协调任务的分布式层次控制器。同时利用模拟函数和共同Lyapunov函数,进一步分析了多个体系统在切换拓扑下的集体行为。     

有向网络下非线性多智能体系统的协调跟踪

基于一致性理论,在有向拓扑结构下研究非线性多智能体系统的协调跟踪控制问题．考虑智能体动力学模型为一般且仅满足Lipschitz条件的非线性系统,在仅有部分跟随智能体能获取领航智能体信息的情形下,当领航智能体与跟随智能体之间的拓扑结构具有有向生成树,即存在领航智能体到所有跟随智能体的有向路径时,所设计的分布式控制律可实现所有跟随智能体对领航智能体的跟踪,并指出该拓扑结构是系统实现跟踪的一个必要条件．最后,仿真实验验证了所设计控制算法的有效性．     

分布式优化的多智能体方法

分布式优化作为分布式协调控制领域中的一个基本而重要的研究课题,近年来,不同领域的众多学者对其产生了广泛的研究兴趣.本文总结归纳了分布式优化的研究现状和近期的研究成果,重点对离线分布式优化和在线分布式优化进行了阐述,并从算法设计和收敛性分析这两个角度进行了剖析.特别地,针对一类混合均衡问题,本文介绍了一类分布式求解算法.最后,阐述了当前尚未解决的问题和未来的研究方向.     

非线性多智能体磁滞系统的分布式输出反馈渐近一致控制

针对一类具有磁滞输入且状态未知的非线性多智能体系统,本文提出了一种基于领导者–跟随者的分布式输出反馈渐近一致自适应控制方案.首先,构造了具有动态高增益的K-滤波器以估计多智能体系统的未知状态.然后,采用一种新型的动态面控制策略设计控制器.不同于传统动态面控制策略所采用的一阶低通滤波器,本文设计了含正时变积分函数的非线性...     

多智能体系统中的分布式强化学习研究现状

对目前世界上分布式强化学习方法的研究成果加以总结, 分析比较了独立强化学习、社会强化学习和群体强化学习三类分布式强化学习方法的特点、差别和适用范围, 并对分布式强化学习仍需解决的问题和未来的发展方向进行了探讨.     

Event-triggered distributed containment control of heterogeneous linear multi-agent systems by an output regulation approach

In this paper, the event-triggered distributed containment control of heterogeneous linear multi-agent systems in the output regulation framework is studied. The leaders are treated as exosystems and the containment control problem will be converted into an output regulation problem. An event-triggered protocol is then designed for each follower by the output information of neighbours. It is proved that the followers can asymptotically converge to the dynamic convex hull spanned by multiple leaders under the designed protocol and triggered strategy. Furthermore, it is shown that the proposed protocol and triggered condition can exclude Zeno behaviour, so the feasibility of the control strategy is verified. Finally, a numerical simulation is given to illustrate the effectiveness of the proposed protocol.     

多智能体网络系统的自适应协调控制

为实现多智能体网络系统的协调控制,设计了一种新型的带有自适应协调器的控制器.基于动态图建立了多智能体网络系统的模型,并考虑了系统的非线性互联和不可避免存在的时变时滞.应用分布式控制策略,设计了自适应参数估计的协调器,用于调节智能体之间的互联强度,使网络达到稳定的预设水平.并基于Lyapunov-Kra-sovskii泛函和自适应动态偏差反馈控制技术,根据拉萨尔不变集原理证明了偏差控制系统的渐近收敛性.这种控制方法,可在系统参数不确定的情况下,同时完成参数估计和协调控制.所设计的控制律和自适应律简单,易于实现,仿真示例验证了所提方法的有效性.     

Distributed output regulation for linear multi-agent systems with communication delays

In this paper, a distributed output regulation approach is presented for the cooperative control of linear multi-agent systems in the presence of communication delays. Both dynamic state and output feedback control laws are designed for achieving the property of output regulation. Sufficient conditions for the existence of these control laws are provided in terms of linear matrix inequalities. Simulation results are given to support the efficiency of the proposed distributed output regulation approach.     

Distributed event-triggered output regulation of multi-agent systems

Motivated by the future use of embedded microprocessors with limited resources and limited computational resources, the distributed output regulation with event-driven strategies problem of linear multi-agent systems is considered in this paper. The main task is to design distributed feedback by employing event-triggered technique for multi-agent systems such that all agents can track an active leader, and/or distributed disturbance rejection. Both leader and disturbance are generated by some external system (exosystem). Both distributed static and dynamic feedback with event-triggered strategy are constructed here. Then, the input-to-state stability of the closed-loop multi-agent system is analysed. Finally, a numerical example is given to validate the proposed control.     

Output feedback distributed optimization algorithms of second-order Lipschitz nonlinear multi-agent systems

This paper introduces output feedback distributed optimization algorithms designed specifically for second-order nonlinear multi-agent systems. The agents are allowed to have heterogeneous dynamics, characterized by distinct nonlinearities, as long as they satisfy the Lipschitz continuity condition. For the case with unknown states, nonlinear state observers are designed first for each agent to reconstruct agents' unknown states. It is proven that the agents' unknown states are estimated accurately by the developed state observers. Then, based on the agents' state estimates and the gradient of each agent local cost function, a kind of output feedback distributed optimization algorithms are proposed for the considered multi-agent systems. Under the proposed distributed optimization algorithms, all the agents' outputs asymptotically approach the minimizer of the global cost function which is the sum of all the local cost functions. By using Lyapunov stability theory, convex analysis, and input-to-state stability theory, the asymptotical convergence of the output feedback distributed optimization closed-loop system is proven. Simulations are conducted to validate the efficacy of the proposed algorithms.     

Observer-based distributed adaptive iterative learning control for linear multi-agent systems

This paper investigates the consensus problem for linear multi-agent systems from the viewpoint of two-dimensional systems when the state information of each agent is not available. Observer-based fully distributed adaptive iterative learning protocol is designed in this paper. A local observer is designed for each agent and it is shown that without using any global information about the communication graph, all agents achieve consensus perfectly for all undirected connected communication graph when the number of iterations tends to infinity. The Lyapunov-like energy function is employed to facilitate the learning protocol design and property analysis. Finally, simulation example is given to illustrate the theoretical analysis.     

Distributed formation output regulation of switching heterogeneous multi-agent systems

In this article, the distributed formation output regulation problem of linear heterogeneous multi-agent systems with uncertainty under switching topology is considered. It is a generalised framework for multi-agent coordination problems, which contains or concerns a variety of important multi-agent problems in a quite unified way. Its background includes active leader following formation for the agents to maintain desired relative distances and orientations to the leader with a predefined trajectory, and multi-agent formation with environmental inputs. With the help of canonical internal model we design a distributed dynamic output feedback to handle the distributed formation output regulation problem.     

Sampling-based self-triggered coordination control for multi-agent systems with application to distributed generators

This work investigates the coordination control problem for multiple distributed generation (DG) units with a hierarchical control structure. At the secondary control level, an event-triggered power sharing strategy based on the concept of multi-agent consensus has been proposed for the DG coordination control. Unlike existing consensus-based DG control approaches, the proposed control algorithm is based on sampled data. Thus the event triggering and controller updating actions can only be executed at the sampling time instants. To further reduce the amount of communication among DGs, the proposed event-triggered algorithm is extended to a self-triggered algorithm, where the inter-agent communication transmission is no longer required to be executed at each sampling time instant. The case study results show that the self-triggered algorithm can achieve nearly the same performance on DG coordination as that of the event-triggered algorithm, while significantly reduces the amount of communication.     

动态事件触发机制下二阶多智能体系统完全分布式控制

本文研究了无向通信拓扑下二阶多智能体系统的一致性问题, 分别针对有领导者和无领导者的情形, 设计 了一类基于辅助动态变量的完全分布式事件触发控制策略, 该策略具有参数较少且易调等特点. 智能体自身的触 发函数满足条件时才向邻居广播自身的状态信息, 有效避免了连续通信, 减少了系统能量耗散. 每个智能体的控制 协议和触发函数都只用到自身的状态和邻居触发时刻的状态, 不涉及邻居的实时状态信息, 也不依赖通信拓扑网络 的任何全局信息. 利用代数图论以及Lyapunov稳定性理论, 证明在所提出的控制策略下, 二阶多智能体系统能够实 现渐近一致性, 且不存在Zeno行为. 仿真示例进一步验证了理论结果的有效性.