邻域交互结构优化的多智能体快速蜂拥控制算法

针对多智能体系统在动态演化过程中容易出现的"局部聚集"现象,融 合复杂网络中的拓扑结构优化理论与多智能体系统协调蜂拥控制研究,提出了一种基 于邻域交互结构优化的多智能体快速蜂拥控制算法.该算法首先从宏观上分析多智 能体的局部聚集现象,利用社团划分算法将局部相对密集的多个智能体聚类成一个 社团,整个多智能体系统可以划分成多个相对稀疏的社团,并为每个社团选择度 最大的个体作为信息智能体,该个体可以获知虚拟领导者信息;随后从多智能体 系统中不同社团相邻个体间的局部交互结构入手,取消社团间相邻个体的交 互作用,设计仅依赖于社团内部邻居个体交互作用的蜂拥控制律;理论分 析表明,只要每个社团存在一个信息智能体,在虚拟领导者的引导作用下,整个多 智能体系统就可以实现收敛的蜂拥控制行为;仿真实验也证实了对多智 能体系统进行邻域交互结构优化可以有效提高整个系统的收敛速度.     

Flocking of Multi-Agents With a Virtual Leader

All agents being informed and the virtual leader traveling at a constant velocity are the two critical assumptions seen in the recent literature on flocking in multi-agent systems. Under these assumptions, Olfati-Saber in a recent IEEE Transactions on Automatic Control paper proposed a flocking algorithm which by incorporating a navigational feedback enables a group of agents to track a virtual leader. This paper revisits the problem of multi-agent flocking in the absence of the above two assumptions. We first show that, even when only a fraction of agents are informed, the Olfati-Saber flocking algorithm still enables all the informed agents to move with the desired constant velocity, and an uninformed agent to also move with the same desired velocity if it can be influenced by the informed agents from time to time during the evolution. Numerical simulation demonstrates that a very small group of the informed agents can cause most of the agents to move with the desired velocity and the larger the informed group is the bigger portion of agents will move with the desired velocity. In the situation where the virtual leader travels with a varying velocity, we propose modification to the Olfati-Saber algorithm and show that the resulting algorithm enables the asymptotic tracking of the virtual leader. That is, the position and velocity of the center of mass of all agents will converge exponentially to those of the virtual leader. The convergent rate is also given.      

Robust integral of sign of error-based distributed flocking control of double-integrator multi-agent systems with a varying virtual leader

This article investigates the flocking control problem of double-integrator multi-agent systems with a virtual leader subject to unknown external disturbances. A robust integral of sign of error (RISE) based control method is leveraged to design a distributed flocking controller with advantages of zero initial input value and continuous control input. By means of a new second-order differential virtual potential field function, and the navigational feedback from a virtual leader, the proposed flocking controller assures agents of velocity consensus with the virtual leader and a quasi -lattice formation within a circular neighborhood centered on the virtual leader. Moreover, this algorithm guarantees collision avoidance and connectivity preservation of a proximity-induced communication topology. Numerical simulations of the algorithm are provided to illustrate the effectiveness of the proposed flocking algorithm.     

基于社区划分的多智能体网络快速蜂拥控制

针对复杂网络社区特性对多智能体系统协同控制效率的影响,面向具有ER(Erdos-renyi)网络或BA (Barabasi-albert)网络性质的多智能体系统,提出一种基于社区划分的快速蜂拥控制算法.该算法充分考虑社区内个体的相对密集特性,通过在社区间引入虚拟领导者作用,避免系统在演化过程中因通信受限而导致的“分块”现象,可有效提高系统拓扑的代数连通度.仿真结果表明:具有相应性质的多智能体系统蜂拥行为的收敛速度与ER和BA网络的平均度以及BA网络度分布的幂指数正相关;优化社区个数有利于提高蜂拥收敛速度.     

Flocking of Multi-agent Systems with Unknown Nonlinear Dynamics and Heterogeneous Virtual Leader

This paper investigates the flocking control of multi-agent systems with unknown nonlinear dynamics while the virtual leader information is heterogeneous. The uncertain nonlinearity in the virtual leader information is considered, and the weaker constraint on the velocity information measurements is assumed. In addition, a bounded assumption on the unknown nonlinear dynamics is also considered. It is weaker than the Lipschitz condition adopted in the most flocking control methods. To avoid fragmentation, we construct a new potential function based on the penalty idea when the initial network is disconnected. A dynamical control law including a adjust parameter is designed to achieve the stable flocking. It is proven that the velocities of all agents approach to consensus and no collision happens between the mobile agents. Finally, several simulations verify the effectiveness of the new design, and indicate that the proposed method has high convergence and the broader applicability in practical applications with more stringent restrictions.

     

多智能体多目标跟踪算法研究

研究了多智能体同时跟踪多个目标的群集算法,采用智能体的编号对目标数求余数的方法来确定各个智能体的跟踪目标。对于势场函数,根据智能体目标是否相同建立了两种势场函数,并在此基础上确定动态系统的控制输入,使相同目标的智能体在跟踪目标的过程中形成群集,而不同目标的智能体相互分离;用李亚普诺夫稳定性理论分析了算法的收敛性。仿真结果表明,所提算法可有效解决多目标跟踪问题。     

A local flocking algorithm of multi-agent dynamic systems

In this paper, the local flocking of multi-agent systems is investigated, which means all agents form some groups of surrounding multiple targets with the partial information exchange. For the purpose of realising local multi-flocking, a control algorithm of local flocking is proposed, which is a biologically inspired approach that assimilates key characteristics of flocking and anti-flocking. In the process of surrounding mobile targets through the control algorithm, all agents can adaptively choose between two work modes to depend on the variation of visual field and the number of pursuing agents with the mobile target. One is a flocking pursuing mode which is that some agents pursue each mobile target, the other is an anti-flocking searching mode that means with the exception of the pursing agents of mobile targets, other agents respectively hunt for optimal the mobile target with a closest principle between the agent and the target. In two work modes, the agents are controlled severally via the different control protocol. By the Lyapunov theorem, the stability of the second-order multi-agent system is proven in detail. Finally, simulation results verify the effectiveness of the proposed algorithm.     

Distributed adaptive flocking of robotic fish system with a leader of bounded unknown input

A distributed leader-follower flocking problem of multiple robotic fish governed by extended second-order unicycles is studied in this paper. The multi-agent system consists of only one leader with pre-appointed and bounded speeds. A distributed flocking algorithm on the basis of the combination of consensus and attractive/repulsive functions is investigated, in which adaptive strategy is adopted to compute the weight of the velocity coupling strengths. The proposed control algorithm enables followers to asymptotically track the leader’s varying velocities and approach the equilibrium distances with their neighbors. Furthermore, the arbitrarily-shaped formation flocking problem of the system can also be solved by adding the information of a desired formation topology to the potential function term. Finally, simulations are carried out to verify the effectiveness of the proposed theoretical results.     

Fuzzy Policy Reinforcement Learning in Cooperative Multi-robot Systems

A multi-agent reinforcement learning algorithm with fuzzy policy is addressed in this paper. This algorithm is used to deal with some control problems in cooperative multi-robot systems. Specifically, a leader-follower robotic system and a flocking system are investigated. In the leader-follower robotic system, the leader robot tries to track a desired trajectory, while the follower robot tries to follow the reader to keep a formation. Two different fuzzy policies are developed for the leader and follower, respectively. In the flocking system, multiple robots adopt the same fuzzy policy to flock. Initial fuzzy policies are manually crafted for these cooperative behaviors. The proposed learning algorithm finely tunes the parameters of the fuzzy policies through the policy gradient approach to improve control performance. Our simulation results demonstrate that the control performance can be improved after the learning.     

Adaptive Flocking Control with a Minority of Informed Agents

In this paper, we address the flocking problem of multiple dynamic mobile agents with a virtual leader in a dynamic proximity network. To avoid fragmentation, we propose a novel flocking algorithm that consists of both an adaptive controller for followers and a feedback controller for the virtual leader. Based on our algorithm, all agents in the group can form a network, maintain connectivity, and track the virtual leader, even when only a minority of agents have access to the information of the virtual leader. Finally, several convincing simulation results are provided that demonstrate 2‐D flocking of a group of agents using the proposed algorithm.     

Second-order tracking control for leader-follower multi-agent flocking in directed graphs with switching topology

This paper investigates the flocking problem for leader-follower multi-agent systems in directed graphs with switching topology. A decentralized state control rule, namely, a second-order protocol, is designed for each agent to track the leader. And it is proved that the proposed control scheme can effectively estimate the tracking error of each agent when the leader is active. Particularly, to ensure the tracking error can be estimated, the following two questions are solved: (1) How many agents are needed to connect to the leader? (2) How should these connections be distributed? Finally, a simple example is also given to verify the effectiveness of the proposed theorems.     

Adaptive consensus of multi-agents in networks with jointly connected topologies

In this paper, the consensus problem of multi-agent following a leader is studied. An adaptive design method is presented for multi-agent systems with non-identical unknown nonlinear dynamics, and for a leader to be followed that is also nonlinear and unknown. By parameterizations of unknown nonlinear dynamics of all agents, a decentralized adaptive consensus algorithm is proposed in networks with jointly connected topologies by incorporating local consensus errors in addition to relative position feedback. Analysis of stability and parameter convergence of the proposed algorithm are conducted based on algebraic graph theory and Lyapunov theory. Finally, examples are provided to validate the theoretical results.     

Flocking shape analysis of multi-agent systems

In this paper,we consider the shape control in flocking behavior of a multi-agent system with a virtual leader.Besides the traditional flocking control terms,which include a gradient-based term,a velocity consensus term and a navigational feed-back in general,a new piecewise smooth neighbor-based local controller is added to regulate the configuration to the desired flocking shape.All agent velocities approach the desired velocity asymptotically,while collisions among agents can be avoided.Furthermore,based...     

Flocking of multi‐agent dynamical systems with intermittent nonlinear velocity measurements

In this paper, the problem of flocking control in networks of multiple dynamical agents with intermittent nonlinear velocity measurements is studied. A new flocking algorithm is proposed to guarantee the states of the velocity variables of all the dynamical agents to converge to consensus while ensuring collision avoidance of the whole group, where each agent is assumed to obtain some nonlinear measurements of the relative velocity between itself and its neighbors only on a sequence of non‐overlapping time intervals. The results are then extended to the scenario of flocking with a nonlinearly dynamical virtual leader, where only a small fraction of agents are informed and each informed agent can obtain intermittent nonlinear measurements of the relative velocity between itself and the virtual leader. Theoretical analysis shows that the achieved flocking in systems with or without a virtual leader is robust against the time spans of the agent speed‐sensors. Finally, some numerical simulations are provided to illustrate the effectiveness of the new design. Copyright © 2011 John Wiley & Sons, Ltd.     

A connectivity-preserving flocking algorithm for multi-agent systems based only on position measurements

Most existing flocking algorithms rely on information about both relative position and relative velocity among neighbouring agents. In this article, we investigate the flocking problem with only position measurements. We propose a provably-stable flocking algorithm, in which an output vector is produced by distributed filters based on position information alone but not velocity information. Under the assumption that the initial interactive network is connected, the flocking algorithm not only can steer a group of agents to a stable flocking motion, but also can preserve the connectivity of the interactive network during the dynamical evolution. Moreover, we investigate the flocking algorithm with a virtual leader and show that all agents can asymptotically attain a desired velocity even if only one agent in the team has access to the information of the virtual leader. We finally show some numerical simulations to illustrate the theoretical results.     

Consensus of linear multi-agent systems with reduced-order observer-based protocols

This paper considers the consensus problems for both continuous- and discrete-time linear multi-agent systems with directed communication topologies. Distributed reduced-order observer-based consensus protocols are proposed, based on the relative outputs of neighboring agents. A multi-step algorithm is presented to construct a reduced-order protocol, under which a continuous-time multi-agent system whose communication topology contains a directed spanning tree can reach consensus. This algorithm is further modified to achieve consensus with a prescribed convergence rate. These two algorithms have a favorable decoupling property. In light of the modified algebraic Riccati equation, an algorithm is then given to construct a reduced-order protocol for the discrete-time case.     

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.     

离散时变多智能体系统有限时间一致性迭代学习控制

针对一类离散时变多智能体系统,通过引入虚拟领导者产生期望轨迹的方法,将虚拟领导者和所有智能体组成固定的拓扑结构,在此基础上,提出一种离散时间迭代学习控制算法.该算法对多智能体系统中的每个智能体都设计一个控制器,各控制器都是利用上一次迭代时,该智能体与虚拟领导者之间的跟踪误差和该智能体与相邻智能体之间的跟踪误差,通过拓扑结构中通信权值的组合不断修正上一次的控制律,从而获得理想控制律.同时,基于范数理论严格证明所提出算法的收敛性,并给出算法在$\lambda$-范数意义下的收敛条件.该算法能够使离散时变多智能体的输出随着迭代次数的增加在有限时间区间内完全跟踪期望轨迹.理论分析和仿真结果都表明了所提出算法的有效性.     

Leader-following consensus of single-integrator multi-agent systems under noisy and delayed communication

This paper proposes a leader-following consensus control for continuous-time single-integrator multi-agent systems with multiplicative measurement noises and time-delays under directed fixed topologies. Each agent in the team receives imprecise information states corrupted by noises from its neighbours and from the leader; these noises are depending on the agents’ relative states information. Moreover, the information states received are also delayed by constant or time-varying delays. An analysis framework based on graph theory and stochastic tools is followed to derive conditions under which the tracking consensus of a constant reference is achieved in mean square. The effectiveness of the proposed algorithms is proved through some simulation examples.     

具有时变通信延迟的多智能体系统改进蜂拥控制

针对不确定非线性二阶多智能体系统中存在的时变通信时延和未知干扰问题,提出了一种鲁棒自适应蜂拥控制规律。为了使二阶多智能体系统能够具有更好的抗干扰能力,设计了基于智能体位置状态信息和速度状态信息的鲁棒自适应算子,实现了系统在时变通信时延扰动下的分布控制。通过使用Lyapunov-Krasovskii方法构造能量函数,证明了多智能体系统的网络连通性,智能体的速度收敛于虚拟领导者的速度,并给出了具有时变通信时延的多智能体系统收敛条件。仿真实验结果表明,在不同干扰强度和不同通信时延下系统均能实现快速收敛,形成稳定的拓扑结构,证明所提方法正确有效。