非仿射非线性多智能体系统迭代学习一致跟踪

为了解决非仿射非线性多智能体系统在给定时间区间上一致性完全跟踪问题,基于迭代学习控制方法设计一种分布式一致性跟踪控制算法.首先,由引入的虚拟领导者与所有跟随者组成多智能体系统的通信拓扑,其中虚拟领导者的作用是提供期望轨迹.然后,在只有部分跟随者能够获得领导者信息的条件下,利用每个跟随者及其邻居的跟踪误差构造每个跟随者的迭代学习一致性跟踪控制器.同时采用中值定理将非仿射非线性多智能体系统转化仿射形式,并基于压缩映射方法证明所提算法的收敛性,给出算法的收敛条件.理论分析表明,在智能体的非线性函数未知情况下,利用所提算法可以使非仿射非线性多智能体系统在给定时间区间上随迭代次数增加逐次实现一致性完全跟踪.最后,通过仿真算例进一步验证所提算法的有效性.     

采用超节点协同的多智能体系统一致性算法

针对如何提高多智能体系统达到一致性的收敛速度的问题,提出了一种采用超节点协同的多智能体系统一致性算法.新算法对多智能体系统建立图信号模型,在图中选出超节点进行协同,有效提高一致性收敛速度.首先利用单跳采样算法对图进行超节点的选取和局部集的划分,并对局部集内的节点进行一次协同.然后超节点之间进行边的连接得到粗化图,用粗化图的拉普拉斯矩阵特征值设计图滤波器的系数.最后超节点的信号经过图滤波器迭代达到平均值后,传输给其一阶邻居节点,使所有节点达到平均一致.仿真结果表明所提算法能够最终实现平均一致性,与现有方法相比,可以显著提高收敛速度,并减少计算量.     

输出饱和多智能体系统的迭代学习趋同跟踪控制

针对带有输出饱和的多智能体系统有限时间趋同跟踪控制问题,提出了一种分布式迭代学习控制算法.首先假设多智能体系统具有固定拓扑结构,且仅有部分智能体可获取到期望轨迹信息.基于输出约束条件构造一致性跟踪误差,在此基础上设计了P型迭代学习控制率.然后采用压缩映射方法给出了一个算法收敛的充分条件,并在理论上证明了跟踪误差的收敛性.最后,将理论结果推广至具有随机切换拓扑结构的多智能体系统中.仿真结果验证了所提出算法的有效性.     

测量受限线性多智能体系统一致性迭代学习控制

本文针对通讯拓扑同时沿时间轴和迭代轴切换且存在测量受限的情形,研究了基于迭代学习控制方法的连续线性多智能体系统输出一致性跟踪问题.在系统通信拓扑始终含有以虚拟领航者为根节点的生成树,以及所有智能体初态在每次迭代均可重置的条件下,针对跟随者能够获得的局部信息而设计了测量受限分布式输出一致性协议.然后,利用λ范数的方法和圆...     

切换拓扑下测量受限多智能体系统一致性迭代学习控制

针对通信拓扑至少含有一个沿迭代轴的联合生成树且同时沿有限时间轴和无限迭代轴切换的情况,文本研究了存在测量受限的连续线性多智能体系统输出一致性迭代学习控制问题.首先,文章采用迭代学习控制方法设计了一种基于跟随者局部信息的分布式输出一致性协议.然后,给出了系统可解输出一致性问题的两个充分性条件,其中之一可使跟随者实时获取迭代学习增益,避免了全局信息对学习增益设计的影响,且保证了算法的分布式实现.接着,利用λ范数理论和圆盘定理严格证明了所设计算法的收敛性.最后,通过实例仿真验证了所得结论的有效性.     

执行器饱和多智能体系统的自适应学习一致性算法

针对一类受到执行器饱和高阶多智能体系统在有限时间区间[0,T]上的精确一致性问题,利用自适应迭代学习控制的方法,设计了具有全饱和差分型自适应更新律的时变增益.通过构造适当的复合能量函数,严格证明了一致性误差向量随着迭代次数趋于无穷而一致趋于零,得到了不依赖于网络通信拓扑矩阵特征值的自适应学习一致性算法.最后,给出一个仿真例子,仿真结果说明了所提算法的有效性.     

基于中间状态值的多智能体系统安全一致性控制

针对存在恶意攻击的多智能体系统一致性控制问题,提出一种快捷有效的安全一致性算法.采用选取中间值的筛选方法,将同一时刻采集到的邻居信息值按从小到大序列排序,选取位于中间序列的信息值用于节点自身的状态更新,该算法较传统一致性算法减少了计算复杂度,同时降低了系统所需较强的网络连通条件和信息储备所需的资源,使得整个系统变得更加简单、灵活.利用迭代学习和凸包条件,通过创建具有与原系统有向图相同连通条件的虚拟网络拓扑图,证明了系统在满足特定的网络拓扑的条件下,能够实现安全一致.仿真结果验证了所提出算法的有效性.     

基于Q-learning的离散时间多智能体系统一致性

针对模型未知的一类离散时间多智能体系统,本文提出了一种Q-learning方法实现多智能体系统的一致性控制.该方法不依赖于系统模型,能够利用系统数据迭代求解出可使给定目标函数最小的控制律,使所有智能体的状态实现一致.通过各个智能体所产生的系统数据,采用策略迭代的方法实时更新求解得到多智能体系统的控制律,并对所提Q-le...     

基于k-shell分解的多智能体牵制控制算法

针对多智能体网络在牵制控制过程中存在的网络分裂现象,考虑到牵制节点选择对多智能体收敛速度的影响,提出一种基于k-shell分解的牵制控制算法.首先根据节点连通度划分子网;然后提出基于k-shell分解的牵制节点选择方法;最后完成多智能体的牵制控制.理论推导证明,采用该算法后整个智能体网络最终将形成一个子网.分析对比3种牵制控制算法,通过实验仿真结果验证所提出算法能够实现多智能体的一致性,有利于提高多智能体的收敛速度.     

基于迭代学习的部分非正则多智能体编队控制

针对一类具有任意初始状态的部分非正则多智能体系统,提出一种迭代学习控制算法.该算法将具有固定拓扑结构的多智能体编队控制问题转化为广义上的跟踪问题,即让领导者跟踪给定的期望轨迹,而跟随者要始终保持预定队形对某一智能体进行跟踪,并将该智能体作为自身的领导者.同时,为了使每个智能体在任意初始状态下都能按照期望队形进行编队,对每个智能体的初始状态设计迭代学习律,并从理论上对算法的收敛性进行严格证明,给出算法收敛的充分条件.所提出的算法对于各个智能体在任意初始位置条件下均能实现在有限时间区间内系统的稳定编队.最后,通过仿真算例进一步验证了所提出算法的有效性.     

无速度输入多智能体系统的一致性

本文研究了有无引导者的多智能体系统在非线性协议下的一致性问题.当智能体速度信息无法获知时,分别针对有无引导者的多智能体系统设计了包含辅助系统和智能体相对位移信息的非线性分布式协议.借助图论、Lyapunov稳定性理论、Barbalat引理等方法,推导出有无引导者的多智能体系统在连通无向通讯网络中实现一致的充分条件,其次,设计了一种新的能使引导–追随者多智能体系统在有向通讯网络中实现期望一致的协议.最后,数值仿真验证了结果的正确性.     

Iterative learning control with input sharing for multi-agent consensus tracking

In this paper, a novel iterative learning control (ILC) scheme with input sharing is presented for multi-agent consensus tracking. In many ILC works for multi-agent coordination problem, each agent maintains its own input learning, and the input signal is corrected by local measurements over iteration domain. If the agents are allowed to share their learned inputs among them, the strategy can improve the learning process as more learning resources are available. In this work, we develop a new type of learning controller by considering the input sharing among agents, which includes the traditional ILC strategy as a special case. The convergence condition is rigorously derived and analyzed as well. Furthermore, the proposed controller is extended to multi-agent systems under iteration-varying graph. It turns out that the developed controller is very robust to communication variations. In the numerical study, three illustrative examples are presented to show the effectiveness of the proposed controller. The learning controller with input sharing demonstrates not only faster convergence but also smooth transient performance.     

Robust iterative learning protocols for finite-time consensus of multi-agent systems with interval uncertain topologies

This paper is devoted to the robust finite-time output consensus problems of multi-agent systems under directed graphs, where all agents and their communication topologies are subject to interval uncertainties. Distributed protocols are constructed by using iterative learning control (ILC) algorithms, where information is exchanged only at the end of one iteration and learning is used to update the control inputs after each iteration. It is proved that under ILC-based protocols, the finite-time consensus can be achieved with an increasing number of iterations if the communication network of agents is guaranteed to have a spanning tree. Moreover, if the information of any desired terminal output is available to a portion (not necessarily all) of the agents, then the consensus output that all agents finally reach can be enabled to be the desired terminal output. It is also proved that for all ILC-based protocols, gain selections can be provided in terms of bound values, and consensus conditions can be developed associated with bound matrices. Simulation results are given to demonstrate the effectiveness of our theoretical results.     

A Novel Leader-following Consensus of Multi-agent Systems with Smart Leader

This article studies the leader-following consensus problem for mixed-order multi-agent systems with a leader. Different from the traditional leader which is independent of all the other agents, the leader, called smart leader, can obtain and utilize the feedback information from its neighbors at some disconnected time intervals. A new distributed consensus control protocol based on intermittent control is developed for leader-following consensus with a smart leader. Moreover, the smart leader can adjust the control protocol based on the feedback information from its neighbors. With the aid of Lyapunov function, some sufficient conditions are derived for leader-following consensus of multi-agent systems with mixed-order dynamics under fixed directed topology. In addition, the similar results are obtained under switching directed topology. Finally, simulation results are provided to verify the correctness and effectiveness of theoretical results.     

Modified leader-following consensus of time-delay multi-agent systems via sampled control and smart leader

In this paper, the consensus problems of multiple agents with continuous-time single-integrator dynamics are studied, where each agent can obtain the position data of its neighboring agents at discrete-time points by using the periodic sampling technology and zero-order hold circuit. The smart leader is introduced, which can adjust the interaction strength between itself and the target point according to the state errors between itself and its neighboring followers. The modified leader-following consensus problem is defined as the leader-following consensus problem when the smart leader is adopted. Different leader-following consensus protocols are obtained for the multi-agent systems with or without sampling delays. The theoretical results, which are analysed with Lyapunov stability theory, can decrease the tracking error of the system, especially for the multi-agent systems with disturbance generated by actuator faults. Some simulation examples and real experiments are presented for illustration.     

Prescribed performance global stable adaptive neural dynamic surface consensus tracking control of stochastic multi-agent systems with hysteresis inputs and nonlinear dynamics

This paper focuses on the leader-following consensus control problem of stochastic multi-agent systems with hysteresis inputs and nonlinear dynamics. A leader-following consensus scheme is presented for stochastic multi-agent systems directions under directed graphs, which can achieve predefined synchronisation error bounds. By mainly activating an auxiliary robust control component for pulling back the transient escaped from the neural active region, a multi-switching robust neuro adaptive controller in the neural approximation domain, which can achieve globally uniformly ultimately bounded tracking stability of multi-agent systems recently. A specific Nussbaum-type function is introduced to solve the problem of unknown control directions. Using a dynamic surface control technique, distributed consensus controllers are developed to guarantee that the outputs of all followers synchronise with that of the leader with prescribed performance. Based on Lyapunov stability theory, it is proved that all signals in closed-loop systems are uniformly ultimately bounded and all the follower agents can keep consensus with the leader. Two simulation examples are provided to illustrate the effectiveness and advantage of the proposed control scheme.     

多智能体系统自适应跟踪控制

基于带有非线性动态的二阶多智能体系统,研究了在有动态领导者条件下的跟踪一致性问题。假设跟随者只能获取邻居智能体的相对状态信息,只有一部分跟随者可以获得领导者的位置和速度信息,领导者的控制输入非零且不被任何一个跟随者可知。在通信拓扑为无向连通图的条件下,为了避免全局信息的不确定性,设计了分布式自适应控制协议。将系统的一致性问题转化为误差系统的一致性问题,通过Lyapunov稳定性理论和矩阵理论分析得到了该协议使系统达到一致的充分条件。最后用仿真例子证明了设计方法的有效性。     

Consensus in multi-agent systems with nonlinear uncertainties under a fixed undirected graph

This paper presents consensus algorithms by integrating cooperative control and adaptive control laws for multi-agent systems with unknown nonlinear uncertainties. An ideal multi-agent system without uncertainties is introduced first. The cooperative control law, based on an artificial potential function, is designed to make the ideal multi-agent system achieve consensus under a fixed and connected undirected graph. The presence of uncertainties will degenerate the performance, or even destabilize the whole multi-agent system. The L ₁ adaptive control law is therefore introduced to handle unknown nonlinear uncertainties. Two different consensus cases are considered: 1) normal consensus—where all agents reach an agreement on an initially undetermined position and velocity, and 2) consensus with a virtual leader—where all agents’ states converge to the virtual leader’s states. Under a fixed and connected undirected graph, the presented consensus algorithms enable the real multi-agent system to stay close to the ideal multi-agent system which achieves consensus with or without a virtual leader. Simulation results of 2-D consensus with nonlinear uncertainties are provided to demonstrate the presented algorithms.     

带虚拟领导者的多智能体系统的非光滑一致性

研究了在限制交互范围条件下具有一个虚拟领导者的二阶多智能体系统的一致性问题。假设多智能体系统中所有智能体均可以接收到领导者的信息，而智能体之间只有距离在一定范围内才可以进行相互通信。在相对状态反馈的线性一致性协议下，通过李雅普诺夫法，证明了该多智能体系统二阶一致性的充分条件。接着通过仿真实例验证了理论结果，并对该算法作出了总结。     

Observer-based distributed consensus for general nonlinear multi-agent systems with interval control inputs

In this paper, observer-based distributed consensus for general nonlinear multi-agent systems with interval control inputs under strongly connected balanced topology is encountered when the relative states of agents are unavailable or undesirable. Theoretical analysis method is further extended to the case of general nonlinear multi-agent systems under switching setting. Moreover, tracking problem on the leader–follower scenario is also explicitly investigated under a mutual assumption that the communication graph, which represents the interaction among agents, contains a directed spanning tree with the leader as its root. It is shown that the consensus for underlying considered multi-agent systems can be desirable as long as the data missing rate does not exceed a certain threshold. Finally, simulation examples are presented to effectively corroborate the analytical findings.