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通信噪声环境中线性多智能体系统的协同控制
其他题名Coordination Control of Linear Multi-Agent Systems with Communication Noise
王云鹏
学位类型工学博士
导师侯增广
2015-06-02
学位授予单位中国科学院大学
学位授予地点中国科学院自动化研究所
学位专业控制理论与控制工程
关键词多智能体系统 协同控制 均方一致性 几乎必然一致性 包含控制 通信噪声 收敛速度 Multi-agent System Coordination Control Mean Square Consensus Almost Sure Consensus Containment Communication Noise Convergence Rate
摘要无论在自然界还是在工程应用中,都存在大量可以建模为多智能体系统的对象,比如自然界中的鸟群、鱼群、蚁群、蜂群等生物群体;再比如实际应用中的传感器网络、多车辆系统、多机械臂系统、多机器人系统、卫星簇等。由于其广阔的应用前景,近年来针对多智能体系统的研究方兴未艾。在多智能体系统的诸多研究方向中,多智能体系统的协同控制一直是其中的一个热点。常见的协同控制任务包括一致性控制、包含控制、编队控制、覆盖控制等,其中一致性控制是其他控制任务的基础。为了实现协同控制的目标,智能体之间通过一个通信网络交互数据。在复杂的应用环境中,通信网络往往会受到噪声、 时延、丢包等因素的干扰。本文研究了通信噪声环境中多智能体系统的协同控制问题,提出了多种协同控制协议。为了抑制通信噪声的影响,本文在提出的所有控制协议中引入了时变噪声抑制增益。具体来说,本文的创新之处主要包括以下四方面: 1. 针对具有切换通信拓扑的二阶积分多智能体系统在通信噪声环境中的一致性问题,提出了一种基于采样数据的一致性控制协议,并给出了系统实现均方一致性和几乎必然一致性的充分条件。本文证明了,若每一个采样时刻的通信拓扑图都是具有生成树的平衡图,且噪声抑制增益满足随机逼近型条件,则在提出的控制协议下,多智能体系统可实现均方一致性。并且还证明了,当噪声抑制增益取某些特殊形式时,多智能体系统的几乎必然一致性也可实现。 2. 针对噪声环境中一阶积分和二阶积分多智能体系统,提出了解决其包含控制问题的控制协议,并给出了保证多智能体系统实现包含控制的充要条件。在分布式系统中,由于其分布式的特性,所有智能体的噪声抑制增益很难做到严格地同步,彼此之间难免会存在一些差异。因此,本文允许每个智能体拥有独立于其他智能体的噪声抑制增益。并在这种情况下证明了,若所有的增益都是同阶无穷小,则系统能够实现均方包含控制。 3. 针对状态未知的多输入多输出线性多智能体系统的一致性问题,提出了一种基于动态输出反馈的一致性协议,并给出了多智能体系统实现均方一致性和几乎必然一致性的充分条件。本文同时考虑了马尔科夫切换通信拓扑和固定通信拓扑两种情况。对于后者,本文还给出了多智能体系统实现均方一致性的必要条件。 4. 针对噪声环境中具有领航者-跟随者结构的一般线性多智能体系统,提出了一种基于状态反馈的一致性协议,并分析了多智能体系统在此协议下的收敛性和收敛速度。在这部分研究中,我们允许每个智能体拥有独立于其他智能体的噪声抑制增益。证明了,若所有噪声抑制增益都是同阶无穷小,则一般线性多智能体系统可实现均方一致性。并且文中定量地给出了领航者与跟随者之间跟踪误差的期望以及二阶矩的收敛速度。
其他摘要There are many examples of multi-agent system in both nature and engineering, such as bird flocking, fish schooling, multi-robot systems, etc. Due to the broad potential applications, multi-agent systems have drawn more and more attention in the past decade. And the coordination control of multi-agent systems plays a key role in the research of multi-agent systems. In order to achieve the control objective, agents exchange information via a communication network, which is usually corrupted by communication noise, delays and other factors. The most common control objectives of coordination control of multi-agent systems include: consensus, containment, formation, coverage, etc. This thesis aims to study the consensus and containment control of multi-agent systems with communication noise. The contributions of this thesis are summarized as follows: The consensus problem of second-order integral multi-agent systems with communication noise and switching topologies is studied. And a sampled-data based consensus protocol is proposed for solving this problem. It is proved that if 1) all communication topologies are balanced and have spanning trees; 2) the time-varying gain satisfies the stochastic approximation type conditions; then the proposed protocol can solve the mean square consensus problem of second-order integral multi-agent systems with communication noise and switching topologies. Meanwhile, we also proved that the almost sure consensus can also be achieved by using the consensus protocol with some particular types of time-varying gains. The containment control problems of first-order and seconder-order integral multi-agent systems with communication noise is studied. Two types of containment protocols are designed for solving these two problems. It turns out that the proposed protocols can solve the mean square containment problems only if the following condition hold: for each follower there exists at least one directed path from one leader to this follower. Different from most papers, each agent is allowed to has its own time-varying gain. It is proved that if all time-varying gains are infinitesimals of the same order, the mean square containment can still be achieved. The consensus problem of multi-input multi-output multi-agent linear systems with communication noise and Markovian switching topologies is investigated. The state of each agent is assumed to be unavailable. To deal with this difficulty, the state observer is borrowed to estim...
其他标识符201218014628018
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/6742
专题毕业生_博士学位论文
推荐引用方式
GB/T 7714
王云鹏. 通信噪声环境中线性多智能体系统的协同控制[D]. 中国科学院自动化研究所. 中国科学院大学,2015.
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