论文标题

基于设置会员过滤基于离散时间线性多代理系统的领导者 - 乘员同步

Set-Membership Filtering-Based Leader-Follower Synchronization of Discrete-time Linear Multi-Agent Systems

论文作者

Bhattacharjee, Diganta, Subbarao, Kamesh

论文摘要

在本文中,提出了基于设置的滤波器滤波器的固定滤波者同步协议,用于离散时间线性多代理系统,其中,其目的是使代理与领导者同步。由相同的高级离散时间线性动力学控制的代理受到未知但结合的输入干扰的约束。就其自己的状态信息而言,每个代理只能访问被未知但结合的输出干扰损坏的测量输出。同样,代理的初始状态尚不清楚。为了处理所有这些未知数(或不确定性),制定了具有标准卡尔曼滤波器的“校正预测”形式的设置会员过滤器(或状态估计器)。我们认为每个代理都配备了此过滤器,该过滤器估计了代理的状态,并认为代理能够与当地的邻居共享状态估计信息。在本地控制法设计中使用了校正的药物的校正状态估计来进行同步。在适当的条件下,代理与领导者之间的全局分歧误差被证明是有限的。对全局分歧误差规范的上限进行了计算,并显示为单调减少。最后,包括两个仿真示例,以说明所提出的设置会员滤波器和提议的Leader-wriverroter同步协议的有效性。

In this paper, a set-membership filtering-based leader-follower synchronization protocol for discrete-time linear multi-agent systems is proposed wherein the aim is to make the agents synchronize with a leader. The agents, governed by identical high-order discrete-time linear dynamics, are subject to unknown-but-bounded input disturbances. In terms of its own state information, each agent only has access to measured outputs that are corrupted with unknown-but-bounded output disturbances. Also, the initial states of the agents are unknown. To deal with all these unknowns (or uncertainties), a set-membership filter (or state estimator), having the `correction-prediction' form of a standard Kalman filter, is formulated. We consider each agent to be equipped with this filter that estimates the state of the agent and consider the agents to be able to share the state estimate information with the neighbors locally. The corrected state estimates of the agents are utilized in the local control law design for synchronization. Under appropriate conditions, the global disagreement error between the agents and the leader is shown to be bounded. An upper bound on the norm of the global disagreement error is calculated and shown to be monotonically decreasing. Finally, two simulation examples are included to illustrate the effectiveness of the proposed set-membership filter and the proposed leader-follower synchronization protocol.

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