事件触发间歇通讯下多智能体系统的固定时间分布式优化

针对多智能体系统的分布式优化问题,提出一种新的事件触发非周期间歇通讯控制方法,并研究该控制方法下系统的固定时间收敛性.首先,考虑一类更一般的分布式优化问题,其优化目标是局部目标函数的凸组合.其次,为了减少控制过程中智能体之间的通讯花费,设计一种新的事件触发间歇控制协议.通过引入两个辅助动力系统,并运用固定时间稳定性理论、代数图论和不等式放缩技巧,证明智能体的状态在固定时间内达到一致并渐近收敛到优化问题的最优解.结合事件触发条件以及间歇控制机制,排除控制过程中的Zeno行为.最后,通过数值仿真验证所得结论的有效性.     

基于事件触发的非线性多智能体系统的固定时间分群一致性

结合事件触发控制方法研究非线性多智能体系统的固定时间分群一致性问题.提出一种非线性分布式事件触发分群一致性控制协议,并给出状态信息触发控制器更新的条件.该控制协议不受入度平衡条件限制,且只需自身状态信息与邻居智能体进行通信即可在固定时间内快速实现分群一致性.系统收敛时间与智能体的初始状态无关,可有效降低系统控制器更新频率和系统的资源消耗.结合代数图论、矩阵分析及Lyapunov稳定性理论,证明在所提出协议作用下,多智能体系统在固定时间内能够实现分群一致性,且不存在Zeno行为.最后,通过仿真实例检验了理论结果的可行性.     

数据丢包下事件驱动的非线性多智能体迭代学习控制

针对具有随机链路丢包、通信带宽受限以及模型未知的非线性多智能体一致性问题, 提出一种事件驱动的分布式无模型迭代学习控制策略. 首先建立系统的事件驱动决策机制, 给出基于输出信息的通信触发条件, 当该条件满足时触发事件, 各智能体间进行通信, 不满足条件时则不通信, 从而能够有效减少智能体间的大量通信和能量耗散. 其次, 使用伪偏导数将非线性系统沿迭代轴动态线性化, 借助邻居在前一步事件触发时的输出信息设计随机链路丢包补偿机制, 再结合事件驱动通信机制设计分布式控制协议. 在此基础上, 使用压缩映射原理分析算法收敛性能, 仿真结果表明随着迭代次数的增加, 事件触发间隔变大, 所有的智能体将完成对期望轨迹的跟踪.     

基于鞍点法的自适应分布式资源分配算法

研究一类带有不等式约束为凸函数的多智能体系统分布式资源分配问题.在资源分配问题中,各智能体拥有仅自身可知的局部成本函数和局部凸不等式约束.分布式资源分配旨在如何利用智能体间的信息交互设计一种分布式优化算法,完成定量资源分配的同时还保证最小化全局成本函数.针对该问题,基于卡罗需-库恩-塔克条件和比例积分控制思想,首先提出一种自适应分布式优化算法,其中凸不等式约束的对偶变量可实现自适应获取;然后,为了降低系统的通信资源消耗,设计一种动态事件触发控制策略以实现离散时间通信的分布式资源分配算法;最后,通过数值仿真验证所设计算法的有效性.     

组合连通拓扑下基于事件触发的多智能体快速一致性算法

针对组合连通拓扑下多智能体系统控制过程中存在通信和计算资源损耗大以及系统收敛速度慢等问题,提出一种新的具有状态预测器的事件触发一致性控制协议,通过设计状态预测器使每个智能体都能对其邻居智能体的未来状态作出预测;同时,对于智能个体给出了基于状态信息的事件触发条件,当状态误差满足该条件才触发事件.在该控制策略下多智能体系统可在节约通信和计算资源的同时具有更快的收敛速度.利用Lyapunov稳定性理论和代数图论,证明了所提事件触发控制策略能够有效实现组合连通拓扑结构下的平均一致性,且不存在Zeno行为.仿真实例进一步验证了理论结果的有效性.     

基于事件驱动的多智能体有限时间分群一致控制

针对二阶线性多智能体系统的分群一致控制问题,考虑智能体通信拓扑同时包含协作和对抗关系,提出一种基于事件驱动控制的有限时间分布式领航跟随分群一致性算法,该算法可使多智能体系统在有限时间内实现分群一致,即各子组内的智能体实现状态一致,不同子组收敛至不同一致状态.采用事件驱动控制机制,设计事件驱动函数及事件触发条件,降低智能体控制器更新频率,减少系统能耗.基于代数图论和李雅普诺夫稳定性理论推导出系统的有限时间稳定性条件,通过巧妙构造Lyapunov函数,给出系统有限收敛时间的显式估计,同时证明在所提出的事件驱动机制下,每个智能体相邻触发时间间隔有严格的正下界,即避免了芝诺行为.仿真实验验证了所提出的有限时间事件驱动分群一致控制算法的有效性.     

周期事件触发的多智能体分布式凸优化

基于周期事件触发机制,研究了具有时变时滞的多智能体系统在强连通有向拓扑下的分布式凸优化问题,提出了一种分布式事件触发零梯度和算法。与时间触发的分布式优化算法相比,该算法可以降低网络系统中的通信负载,具有能耗低和通信成本低的优点。此外,还证明了智能体的状态渐近收敛到全局最优点。由于事件仅在周期时刻进行检验,那么相邻事件触发时刻的时间间隔的下界是采样周期h,可以直接排除Zeno行为。最后通过数值模拟说明了理论结果的有效性。     

多智能体系统离散时间一致性问题中的参数设计

研究二阶多智能体系统在固定、有向通信拓扑条件下的离散时间一致性问题.针对每个智能体,采用基于速度、位置的分布式控制协议,分析速度、位置增益系数以及采样周期等参数对系统一致性的影响.结果表明,要实现二阶多智能体系统的一致性控制,在保持个体之间通信拓扑有生成树的同时,控制协议中的各个参数还必须满足一定的约束条件.系统拉普拉斯矩阵的特征值和采样周期对系统能否实现渐近一致起到了关键的作用.最后,给出一致性控制中参数设计的一种可行方法,并通过仿真对理论分析的正确性进行验证.     

网络攻击下多智能体系统动态事件触发一致性

研究网络攻击下一般线性多智能体系统的动态事件触发一致性问题.考虑多智能体系统在受到网络攻击后,被攻击节点的状态会改变,导致与其相应的连接无法工作,设计修复策略恢复被攻击节点及其相应的连接,给出网络攻击下分布式事件触发控制协议.在静态事件触发机制基础上,通过引入动态阈值参数,提出动态事件触发机制.进一步,利用图论、线性矩阵不等式和李雅普诺夫函数方法,给出网络攻击下实现多智能体系统一致性的充分条件,并证明在所提出的动态事件触发条件下,能够有效避免芝诺行为.最后,通过仿真例子来验证理论结果的有效性.     

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.     

Sampled-data based distributed convex optimization with event-triggered communication

This paper studies the distributed convex optimization problem for multi-agent systems over undirected and connected networks. Motivated by practical considerations, we propose a new distributed optimization algorithm with event-triggered communication. The proposed event detection is decentralized, sampled-data and not requires periodic communications among agents to calculate the threshold. Based on Lyapunov approaches, we show that the proposed algorithm is asymptotically converge to the unknown optimizer if the design parameters are chosen properly. We also give an upper bound on the convergence rate. Finally, we illustrate the effectiveness of the proposed algorithm by a numerical simulation.     

Distributed event-triggered control of multi-agent systems with combinational measurements

This paper studies the distributed rendezvous problem of multi-agent systems with novel event-triggered controllers. We have proposed a combinational measurement approach to event design and developed the basic event-triggered control algorithm. As a result, control of agents is only triggered at their own event time, which reduces the amount of communication and lowers the frequency of controller updates in practice. Furthermore, based on the convergence analysis of the basic algorithm, we have proposed a new iterative event-triggered algorithm where continuous measurement of the neighbor states is avoided. It is noted that the amount of communication among agents has been significantly reduced without obvious negative effects on the control performances. The effectiveness of the proposed strategies is illustrated by numerical examples in 3D spaces.     

基于事件触发的分布式优化算法

本文研究了一类分布式优化问题,其目标是通过局部信息交换使由局部成本函数之和构成的全局成本函数最小.针对无向连通图,我们提出了两种基于比例积分策略的分布式优化算法.在局部成本函数可微且凸的条件下,证明了所提算法渐近收敛到全局最小值点.更进一步,在局部成本函数具有局部Lipschitz梯度和全局成本函数关于全局最小值点是有...     

Distributed event-triggered consensus control for multiple Lur'e nonlinear systems under directed graph

This article addresses the distributed consensus problem of multi-agent systems with Lur'e nonlinear dynamics under directed graphs based on event-triggered strategy. With the state-dependent event-triggered thresholds, a novel distributed event-triggered consensus controller is designed, under which the consensus can be achieved with zero final consensus error. The proposed strategy has several distinguishing features, including the fact that individual agent does not require continuous, or even periodic, communication with their neighbours to update the controller or monitor the triggering condition, and all the required parameters can be locally determined by the agent. Furthermore, we also prove that there is no Zeno behaviour existed. Finally, the simulation example about Chua’s circuit is given to illustrate the theoretical analysis.     

事件驱动的强化学习多智能体编队控制

针对经典强化学习的多智能体编队存在通信和计算资源消耗大的问题,本文引入事件驱动控制机制,智能体的动作决策无须按固定周期进行,而依赖于事件驱动条件更新智能体动作。在设计事件驱动条件时,不仅考虑智能体的累积奖赏值,还引入智能体与邻居奖赏值的偏差,智能体间通过交互来寻求最优联合策略实现编队。数值仿真结果表明,基于事件驱动的强化学习多智能体编队控制算法,在保证系统性能的情况下,能有效降低多智能体的动作决策频率和资源消耗。     

Distributed event‐triggered tracking control with a dynamic leader for multiple Euler‐Lagrange systems under directed networks

The distributed tracking control for multiple Euler‐Lagrange systems with a dynamic leader is investigated in this article via the event‐triggered approach. Only a portion of followers have access to the leader, and the communication topology among all agents is directed that contains a directed spanning tree rooted at the leader. The case that the leader's generalized velocity is constant is first considered, and a distributed event‐based control law is developed by using a velocity estimator. When the leader's generalized velocity is time‐varying, novel distributed continuous estimators are proposed to avoid the undesirable chattering effect while guaranteeing that the estimate errors converge to zeros. With the designed distributed estimators, another distributed event‐based control protocol is provided. Controller update frequency and resource consumption in our work can be reduced by applying the aforementioned two distributed control laws, and the tracking errors can converge to zeros. In addition, it is rigorously proved that no agent exhibits Zeno behavior. Finally, the effectiveness of the proposed distributed event‐based control laws is elucidated by a number of simulation examples.     

Neural network-based event-triggered cluster quasi-consensus for unknown multiagent systems with directed topology

This paper studies cluster quasi-consensus problem for a class of unknown nonlinear multiagent systems (MASs) with directed communication topology. First, a distributed continuous neural network (NN)-based adaptive protocol is presented for solving this problem by introducing reference model to each agent. Then, taking limited communication resource and energy consumption into account, a distributed event-triggered cluster quasi-consensus protocol is proposed. Different from the existing results, two event-triggered mechanisms are constructed in the proposed event-triggered protocol to reduce communication load and control update frequency as possible. The sufficient conditions that guarantee cluster quasi-consensus under the both proposed protocols are obtained, respectively. Zeno behavior is proved to be excluded. Finally, simulation results verify the effectiveness of the proposed protocols.     

Improved leaderless consenus criteria of networked multi-agent systems based on the sampled data

This paper is concerned with leaderless consensus of networked multi-agent systems with Lipschitz-type nonlinear dynamics based on the sampled data. By introducing a weighted average state of all nodes as the virtual leader, the error variables are defined as the distance between agents and the virtual leader. The improvements are done in two directions. First, several consensus conditions are derived by linear matrix inequalities based on Finsler's lemma instead of algebraic criteria based on the general algebraic connectivity. Finsler's lemma is utilised to deal with the linear dependence of the error variables. Second, a distributed event-triggered transmission strategy under the periodic sampling scheme is proposed to reduce the communication between agents and the control updates, which only need to check the event condition at each sampling instant, avoiding the continuous monitor. With the improved strategies, one theorem and two corollaries are derived and a co-design approach of the feedback gain and the event-triggered matrix is also presented. Finally, an example is provided to validate the effectiveness of the theoretical results.     

Edge event-triggered control for multi-agent systems under directed communication topologies

The edge event-triggered consensus control for multi-agent systems with single- or double-integrator dynamics under directed communication topologies is investigated in this paper. With the edge event-triggered sampling strategies, a class of novel consensus protocols is proposed to reduce control update frequency. The main features of the proposed edge event-triggered control include three aspects: (i) measurement errors are defined based on directed information flow and thus they allow that the communication topology is directed, (ii) each individual controller updates its output only when the associated edge event occurs and the control actions are independent of other agents, and (iii) the proposed edge event-triggered control does not exhibit Zeno behaviour and it is shown that the inter-event intervals are lower bounded by a positive constant. In the consensus analysis, matrix transformation techniques are used to convert the high-dimensional system into a low-dimensional system, and some sufficient consensus conditions are derived for the multi-agent systems with single- or double-integrator dynamics. Finally, the effectiveness of the edge event-triggered sampling strategies is illustrated by simulations.     

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.