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.     

事件触发控制下二阶多智能体的量化一致性

针对多智能体系统网络通信过程中信息需要量化的情况,研究了二阶多智能体系统在事件触发控制下的量化一致性。基于事件触发控制策略,提出一致性协议,并采用对数量化器对控制输入进行量化处理。利用Lyapunov稳定性理论,对系统进行一致性分析,得到了多智能体系统渐近趋于一致的充分条件。仿真结果说明了理论分析的有效性。     

仅用位置信息的多智能体自适应蜂拥控制

复杂系统的研究中,提出了自适应调节的多智能体蜂拥控制算法.算法首先通过改进人工势函数来保证网络的连通性.再通过构造分布式观测器,用多智能体之间的位置信息来估计多智能体之间的速度信息;同时对观测器权重参数进行自适应调节来降低蜂拥控制模型的参数不确定性的影响.并且从理论上对算法进行了分析,然后给出计算机仿真,证明了算法的有效性.仿真结果表明,上述算法能够使多智能体经过一定时间的调整达到速度一致并进入稳态,且运动过程中保证网络连通性.     

基于事件触发的时变拓扑多智能体系统编队控制

针对资源受限的时变拓扑多智能体系统的编队控制问题,提出一种基于复合误差信息事件触发机制以减少不必要的信息传输,降低带宽占用,并设计了多智能体系统的编队控制协议和分布式事件触发机制.通过将多智能体系统的编队控制问题转化为闭环延时系统的稳定性问题,构造Lyapunov-Krasovskii函数并利用线性矩阵不等式技术,给出多智能体系统实现编队的充分条件.最后,通过仿真验证了所提出方法的有效性.     

融合局部自适应追踪的多目标牵制蜂拥算法

针对以往的多智能体蜂拥控制算法在考虑单个目标追踪情形时不具普适性,以及现有的多目标蜂拥控制都是基于全局目标信息来进行集中式协调控制,而非基于局部目标信息下的分布式协调控制的问题,提出一种融合局部自适应检测机制的分布式协同牵制蜂拥算法。首先,算法在分离、聚合、速度匹配和引导反馈的基础上,引入局部自适应追踪策略,实现智能体的局部动态跟随运动;其次,受牵制思想启发,根据节点影响力指数评估算法选取m个信息个体分别向m个目标进行多目标追踪,起到模拟外部信息的作用,不同的信息个体会由于局部自适应检测机制间接地引领周围局部个体向不同目标进行追踪;最后,设计一类新的聚集和排斥势能函数,实现相同目标智能体的聚集,以及不同目标智能体的避碰,具有可调参数少和效率高的优势。通过三维仿真实验验证了算法的多目标追踪可行性和有效性。     

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.     

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。     

基于网络一致性的多智能体跟踪控制

基于动态网络一致性理论,研究外部向量场变化情况下的分布式多智能体的队形保持和跟踪控制问题.利用邻近多智能体间共享的信息,智能体以一致协商的合作方式既控制自身运动轨迹,同时又保持了一定的队形.针对多智能体之间形成的最近邻耦合网络拓扑进行分析后,提出了多智能体一致性动态控制律的设计方法,可方便地对系统的极点进行配置.还提出了一种利用分散的队形控制分解中心领航者作用的方法,并给出了系统稳定性的条件.研究结果显示,队形稳定性问题仅受到通讯拓扑结构的影响,可以通过分解降阶的系统解决,而向量场的特性只影响系统跟踪性能.仿真例子显示了方法的正确性.     

不确定非线性多智能体系统的分布式模糊自适应镇定控制

针对一类具有未知非线性和未知参数摄动的非线性多智能体系统, 提出一种分布式模糊自适应镇定控制方法. 基于邻接智能体信息和部分智能体的自身信息, 分别设计静态耦合和动态耦合的分布式模糊自适应控制律. 基于Lyapunov 稳定性理论, 证明了所提出的控制器能使得系统状态最终稳定于原点的邻域内. 仿真实例验证了所提出方法的有效性.

     

异质多智能体系统滞后一致性跟踪控制

针对一阶、二阶混合异质多智能体系统的滞后一致性问题,提出了一种基于牵制控制的分布式滞后一致性控制协议。首先,将滞后一致性分析转化为稳定性证明;然后,利用图论和Lyapunov稳定性理论分析闭环系统稳定性;最后,分别在固定拓扑和切换拓扑结构下给出了基于线性矩阵不等式（LMI）形式的滞后一致性可解的充分条件,从而实现异质多智能体的领导-跟随者滞后一致性。数值仿真结果表明,所提出的滞后一致性控制方法可以使异质多智能体系统实现领导-跟随者滞后一致性。     

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.     

Flocking Algorithms in Networks with Directed Switching Velocity Interaction Topologies

This paper studies flocking algorithms for multi‐agent systems with directed switching velocity interaction topologies. It is assumed that the position information of each agent is available for agents within its neighborhood region of radius r, however, they communicate the velocity information between each other through unidirectional links modeled by a particular class of directed topologies. A new energy function is defined to analyze the global stability and a sufficient condition is derived for asymptotic flocking in the face of switching topologies. The proposed control strategy guarantees the achievement of desired formations, while avoiding collisions among agents. It also ensures velocity agreement under suitable conditions in a variety of real networks with greatly reduced velocity data exchange. Moreover, a leader‐follower framework is formulated for the described class of interaction topologies and it is shown that a more relaxed condition is required to achieve the desired performance. Finally, several simulations are performed to illustrate and confirm the theoretical results obtained.     

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.

     

融合沿墙行为的多移动机器人有序化多模态群集运动控制

一类基于势场原理的群集控制理论正逐步应用于多agent(智能体)/机器人稳定协同运动中.针对群集运动系统在非规则障碍物环境中运行时易出现的局部极小问题,引入基于行为的机器人学理念,构成多移动机器人多模态群集控制系统.在此框架内,仿生的动物沿端行为与有序化群集运动控制策略相融合,实现了多移动机器人系统快速聚合行为与高效避障行为的统一.移动机器人仿真实验验证了该方法的有效性.     

Stable Flocking of Multiple Inertial Agents on Balanced Graphs

In this note, we consider the flocking of multiple agents which have significant inertias and evolve on a balanced information graph. Here, by flocking, we mean that all the agents move with a common velocity while keeping a certain desired internal group shape. We first show that flocking algorithms that neglect agents' inertial effect can cause unstable group behavior. To incorporate this inertial effect, we use the passive decomposition, which decomposes the closed-loop group dynamics into two decoupled systems: a shape system representing the internal group shape and a locked system describing the motion of the center-of-mass. Then, analyzing the locked and shape systems separately with the help of graph theory, we propose a provably stable flocking control law, which ensures that the internal group shape is exponentially stabilized to a desired one, while all the agents' velocities converge to the centroid velocity that is also shown to be time-invariant. This result still holds for slow-switching balanced information graphs. Simulation is performed to validate the theory.     

基于信息耦合度的群集系统自组织分群方法

针对“速度平均”协同机制不能表征群集系统应激分群运动的问题,基于信息熵定义融合邻居速度、距离、数量及自身感知半径的信息耦合度指标,提出一种“min-max”形式的速度协同策略,结合“近距排斥-远距吸引”的位置协同,实现群集系统的自组织应激分群运动。数值仿真分析表明,基于该速度协同机制的群集能够完成一种概率意义上的等规模分群,且其组群效率优于传统基于速度平均机制的群集。     

Flocking for multi-agent systems with optimally rigid topology based on information weighted Kalman consensus filter

This paper investigates the leader-follower flocking problem of multi-agent systems. The leader with input noise is estimated by a proposed continuous-time information weighted Kalman consensus filter (IWKCF) for agents. A novel distributed flocking algorithm based on the IWKCF is further presented to make agents achieve flocking to the leader. It is shown that the proposed flocking algorithm based on the continuous-time IWKCF is asymptotically stable. Applying the topology optimization scheme, the communication complexity of system topologies of multi-agent systems is effectively reduced. Finally, simulations are provided to demonstrate the effectiveness of the proposed results.     

Flocking for multi-agent dynamic systems: algorithms and theory

In this paper, we present a theoretical framework for design and analysis of distributed flocking algorithms. Two cases of flocking in free-space and presence of multiple obstacles are considered. We present three flocking algorithms: two for free-flocking and one for constrained flocking. A comprehensive analysis of the first two algorithms is provided. We demonstrate the first algorithm embodies all three rules of Reynolds. This is a formal approach to extraction of interaction rules that lead to the emergence of collective behavior. We show that the first algorithm generically leads to regular fragmentation, whereas the second and third algorithms both lead to flocking. A systematic method is provided for construction of cost functions (or collective potentials) for flocking. These collective potentials penalize deviation from a class of lattice-shape objects called /spl alpha/-lattices. We use a multi-species framework for construction of collective potentials that consist of flock-members, or /spl alpha/-agents, and virtual agents associated with /spl alpha/-agents called /spl beta/- and /spl gamma/-agents. We show that migration of flocks can be performed using a peer-to-peer network of agents, i.e., "flocks need no leaders." A "universal" definition of flocking for particle systems with similarities to Lyapunov stability is given. Several simulation results are provided that demonstrate performing 2-D and 3-D flocking, split/rejoin maneuver, and squeezing maneuver for hundreds of agents using the proposed algorithms.     

Flocking with connectivity preservation for disturbed nonlinear multi-agent systems by output feedback

This paper considers the problem of flocking with connectivity preservation for a class of disturbed nonlinear multi-agent systems. In order to deal with the nonlinearities in the dynamic of all agents, some auxiliary variables are introduced into the state observer for stability analysis. By proposing a bounded potential function and using adaptive theory, a novel output feedback consensus algorithm is developed to guarantee that the states of all agents achieve flocking with connectivity preservation.