基于一类连续非线性函数的多智能体系统有限时间一致性

针对存在时滞的多智能体系统,提出了基于一类连续非线性函数的有限时间一致性算法.利用Lyapunov有限时间稳定性理论和矩阵理论,给出了这类算法使得系统能够在有限时间内达到一致的充分条件,进而给出了一个满足条件的有限时间一致性算法,并对该算法的收敛性进行分析,得到了系统的收敛时间.数值仿真验证了所提出算法的有效性.     

自适应神经网络的有限时间一致性控制

多智能体系统的有限时间一致性控制问题是指通过设计有效的一致性控制协议,使得多智能体系统中每个智能体的状态趋于一致。针对这一问题,提出一种结合自适应神经网络和滑模控制方法的有限时间一致性控制协议,自适应地在线逼近系统存在的非线性,消除抖振,提高了系统的收敛速度。此外,收敛时间取决于控制器参数,可离线获取。最后通过MATLAB数值仿真,对比验证了所设计协议的有效性。     

完全分布式异构多智能体系统有限时间跟踪

针对异构二阶非线性多智能体系统有限时间跟踪问题,提出一种完全分布式一致性控制方法,消除对于误差上界与Laplacian矩阵特征值等全局信息的依赖.设计一种只包含局部信息的有限时间一致性协议,并提出自适应增益的分布式切换机制,使得各增益以分段常数的方式进行调节,简化了相邻两次切换时间区间内稳定性分析过程.通过反证法证明多智能体系统必将实现有限时间一致性,并且自适应增益保持有界.仿真结果验证了所提出的完全分布式一致性协议的有效性.     

离散时间混合多智能体的拟平均一致性控制

为了研究混合一阶和二阶异质无向多智能体网络离散时间一致性问题,提出了固定和可变拓扑结构的多智能体网络的拟平均一致性协议.根据Lyapunov函数和代数图论,分析了多智能体网络的稳定性,表明在固定和可变拓扑结构的情况下,混合阶多智能体系统可以达到拟平均一致.对6个节点组成的4种不同的无向网络拓扑结构进行了仿真,结果显示了变拓扑离散时间异质多智能体网络的位置和速度均能达到拟平均一致.仿真结果表明,该算法对混合阶多智能体网络的拟平均一致性控制有效.     

FDI攻击下互联电力系统的分布式安全状态估计

针对虚假数据注入(FDI)攻击下的多区域互联电力系统安全状态估计问题,提出一种分布式中间观测器,同时对各区域电力系统的状态、虚假数据注入攻击信号以及负载偏差进行估计.首先,通过将电力系统的状态和虚假数据注入攻击进行增广,得到等价的区域电力系统状态空间模型;然后,基于等价系统模型构建分布式中间观测器,对各个电力子系统分别进行安全状态估计,并设计补偿控制策略以降低虚假数据注入攻击及负载偏差带来的影响;最后,通过算例仿真验证所提出方法的可行性和有效性.     

有限时间内的动车组群分布式协同巡航多智能体控制

本文研究了动车组群系统的移动闭塞协同巡航控制问题,通过设计有限时间分布式协同控制算法实现了多车高速协同巡航.算法取消了动车组中心控制单元,将单列动车组的每个动力单元视为一个智能体,不同动车组之间的智能体形成多智能体群系统.设计了动车组智能体有限时间分布式协同控制算法,该算法首先实现了单列动车组各个动力单元快速追踪到期望的速度,且保证车钩位移在安全范围内,其次该算法还能实现动车组群高速巡航等间距运行,提高了线路利用率的同时避免了碰撞.最后进行了理论分析,证明了算法的稳定性和有限时间收敛性,并提供了仿真结果验证了该算法的有效性.     

离散时间多智能体系统一致性的平均驻留时间条件

研究高阶离散时间线性多智能体系统在有向切换信息拓扑下的状态一致性问题。首先通过提出的线性变换将该一致性问题转换为相应离散时间线性切换系统的渐近稳定性问题。然后借助于切换系统稳定性的平均驻留时间方法,分别得到如下两种情形下该一致性问题可解的充分条件：1)信息拓扑集合中的一部分拓扑是可一致的;2)信息拓扑集合中所有信息拓扑是可一致的。最后通过数值仿真验证了所得理论结果的正确性。     

一种基于离散时间一致性理论的多机器人分布式巡逻算法

在多机器人巡逻任务中,由于通信距离的限制,单个机器人很难获得全局信息。然而,现有的大多数多机器人分布式巡逻算法都要求每个机器人获得其巡逻区域的全局信息进行决策。因此,考虑到通信半径约束和局部信息约束,为了通过相邻机器人之间的交互完成巡逻任务,基于离散时间一致性理论提出了两种巡逻算法。算法1使用全局信息进行决策,算法2基于离散时间一致性理论实现局部信息对全局信息的预测进行决策。通过模拟器Stage对所提算法与对比算法在不同机器人数量、通信半径、地图环境下进行了对比。实验验证了所提出的基于局部信息的分布式多机器人巡逻算法具有与原算法类似的特性和性能,能够使机器人在没有全局信息的情况下判断全局状态,并基于邻居之间的协商完成巡逻任务。     

受攻击信息物理系统的分布式安全状态估计与控制—一种有限时间方法

研究具有非线性耦合特性信息物理系统（Cyber physical systems,CPS）在被攻击情况下的分布式有限时间状态安全估计和控制问题.首先,提出一种由分布式安全测量预选器和有限时间观测器组成的分布式有限时间状态安全估计策略,可确保系统的状态在预设的有限时间之内被准确估计出来.然后,利用获得的安全状态估计,借助反步设计方法,建立一套分布式有限时间安全控制算法.理论分析表明,该方法可以保证系统能在有限时间内实现对给定信号的跟踪.最后,通过微电网系统仿真验证了所提方法的有效性.     

基于梯度估计的多智能体系统有限时间分布式优化

现有多智能体系统分布式优化算法大多具有渐近收敛速度,且要求系统的网络拓扑图为无向图或有向平衡图,在实际应用中具有一定的保守性.本文研究了具有强连通拓扑的多智能体系统有限时间分布式优化问题.首先,基于非光滑分析和Lyapunov稳定性理论设计了一个有限时间分布式梯度估计器.然后,基于该梯度估计器提出了一种适用于强连通有向图的有限时间分布式优化算法,实现了多智能体系统中智能体的状态在有限时间内一致收敛到全局最优状态值.与现有的有限时间分布式优化算法相比,新提出的有限时间优化算法适用于具有强连通拓扑的多智能体系统,放宽了系统对网络拓扑结构的要求.此外,本文基于Nussbaum函数方法对上述优化算法进行了拓展解决了含有未知高频增益符号的多智能体系统分布式优化问题.最后,通过仿真实例对提出的分布式优化算法的有效性进行了验证.     

Nonlinear finite-time bipartite consensus protocol for multi-agent systems associated with signed graphs

In this paper, finite-time multi-agent consensus problems are considered under networks associated with signed graphs whose edge weights can be not only positive but also negative. A nonlinear consensus protocol is proposed to guarantee the states of all agents to converge in a finite time. If the signed graph is structurally balanced, then the final consensus states of all agents are the same in modulus but not in sign. Otherwise, if the signed graph is structurally unbalanced, then the states of all agents converge to zero. Moreover, the final consensus states of agents can be provided uniformly regarding a signed-average quantity that depends on both the initial states of agents and the topology structure of the whole multi-agent network. Numerical simulations illustrate that the protocol is effective in achieving the finite-time consensus of agents under signed graphs and can particularly solve the finite-time average consensus problem of agents when their associated graph has all positive edge weights.     

Distributed average consensus with least-mean-square deviation

We consider a stochastic model for distributed average consensus, which arises in applications such as load balancing for parallel processors, distributed coordination of mobile autonomous agents, and network synchronization. In this model, each node updates its local variable with a weighted average of its neighbors’ values, and each new value is corrupted by an additive noise with zero mean. The quality of consensus can be measured by the total mean-square deviation of the individual variables from their average, which converges to a steady-state value. We consider the problem of finding the (symmetric) edge weights that result in the least mean-square deviation in steady state. We show that this problem can be cast as a convex optimization problem, so the global solution can be found efficiently. We describe some computational methods for solving this problem, and compare the weights and the mean-square deviations obtained by this method and several other weight design methods.     

Distributed robust finite-time nonlinear consensus protocols for multi-agent systems

This paper investigates the robust finite-time consensus problem of multi-agent systems in networks with undirected topology. Global nonlinear consensus protocols augmented with a variable structure are constructed with the aid of Lyapunov functions for each single-integrator agent dynamics in the presence of external disturbances. In particular, it is shown that the finite settling time of the proposed general framework for robust consensus design is upper bounded for any initial condition. This makes it possible for network consensus problems to design and estimate the convergence time offline for a multi-agent team with a given undirected information flow. Finally, simulation results are presented to demonstrate the performance and effectiveness of our finite-time protocols.     

带扰动的多非完整移动机器人分布式有限时间一致性控制

论文研究多个非完整移动机器人在控制输入存在干扰时,有限时间一致性控制问题.利用坐标变换,将多移动机器人系统的一致性问题转化为非完整约束链式系统的一致性问题,在控制输入带有未知有界干扰的条件下,设计了一种分布式控制算法,并利用Lyapunov理论证明了该算法能够使移动机器人的各个状态在有限时间内达到一致.最后通过数值仿真验证了算法的有效性.     

快速平均一致与多采样率下储能系统精准功率分配

针对智能电网中储能系统分布式功率分配问题, 本文提出融合了快速平均一致与多采样率的离散时间控制策略, 使各储能电池以相同相对剩余电量状态(SoC)变化率进行充放电. 为了仅用局部通信得到计算功率分配值需要的全局平均值, 在储能电池拓扑结构已知和未知两种情况下, 本文分别采用了有限时间平均一致算法和具有最优收敛率的平均一致算法. 在储能电池单个控制周期内, 平均一致算法以小于电池控制周期的采样周期进行多步迭代, 从而得到精确的估计值, 实现各储能电池的精准功率分配. 不同于传统连续时间控制方法仅给出平均一致算法的收敛性分析, 本文不仅建立了单个控制周期内快速平均一致算法的收敛率表达式, 而且给出了包含储能电池动力学和平均一致算法的整个控制系统的渐近稳定性分析. 所提方法能够更快速、更精准地实现基于相同相对SoC变化率的功率分配. 最后, 本文通过多组仿真实验验证了所提方法的有效性.     

Step-size sequence design for finite-time average consensus in secure wireless sensor networks

This paper concerns the study of average consensus in wireless sensor networks with aim of providing a way to reach consensus in a finite number of steps. In particular, we investigate the design of consensus protocols when, for security reasons for instance, the underlying graph is constrained to be strongly regular or distance regular. The proposed design method is based on parameters of the intersection array characterizing the underlying graph. With this protocol, at execution time, average consensus is achieved in a number of steps equal to the diameter of the graph, i.e. the smallest possible number of steps to achieve consensus.     

Dynamic consensus estimation of weighted average on directed graphs

Recent applications call for distributed weighted average estimation over sensor networks, where sensor measurement accuracy or environmental conditions need to be taken into consideration in the final consensused group decision. In this paper, we propose new dynamic consensus filter design to distributed estimate weighted average of sensors’ inputs on directed graphs. Based on recent advances in the filed, we modify the existing proportional-integral consensus filter protocol to remove the requirement of bi-directional gain exchange between neighbouring sensors, so that the algorithm works for directed graphs where bi-directional communications are not possible. To compensate for the asymmetric structure of the system introduced by such a removal, sufficient gain conditions are obtained for the filter protocols to guarantee the convergence. It is rigorously proved that the proposed filter protocol converges to the weighted average of constant inputs asymptotically, and to the weighted average of time-varying inputs with a bounded error. Simulations verify the effectiveness of the proposed protocols.     

Distributed fuzzy adaptive event-triggered finite-time consensus tracking control for uncertain nonlinear multi-agent systems with asymmetric output constraint

This article investigates the consensus problem for uncertain nonlinear multi-agent systems (MASs) with asymmetric output constraint. Different from BLF-based constraint consensus tracking control, a novel approach based on nonlinear state-dependent function is proposed to solve the asymmetric output constraint, which need not convert output constraint into tracking error bound. First-order sliding mode differentiator is incorporated into each step of backstepping control design to reduce computation burden. Further, in combination of proposed event-triggered mechanism based on time-varying threshold, a distributed fuzzy adaptive event-triggered finite-time consensus method is developed. It can ensure that the consensus tracking error tends to a small neighbor in a finite time and the asymmetric output constraint of each subsystem is not violated. Two simulations are given to demonstrate the effectiveness of control method.     

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.     

Distributed finite-time consensus tracking for nonlinear multi-agent systems with a time-varying reference state

This paper investigates the consensus tracking problem for nonlinear multi-agent systems with a time-varying reference state. The consensus reference is taken as a virtual leader, whose output is only its position information that is available to only a subset of a group of followers. The dynamics of each follower consists of two terms: nonlinear inherent dynamics and a simple communication protocol relying only on the position of its neighbours. In this paper, the consensus tracking problem is respectively considered under fixed and switching communication topologies. Some corresponding sufficient conditions are obtained to guarantee the states of followers can converge to the state of the virtual leader in finite time. Rigorous proofs are given by using graph theory, matrix theory, and Lyapunov theory. Simulations are presented to illustrate the theoretical analysis.