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Cooperative Fault-Tolerant Output Regulation for Multiagent Systems by Distributed Learning Control Approach

机译:分布式学习控制方法的多算系统合作容错输出调节

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In this article, a new distributed learning control approach is proposed to address the cooperative fault-tolerant output regulation problem for linear multiagent systems with actuator faults. First, a distributed estimation algorithm with an online learning mechanism is presented to identify the system matrix of the exosystem and to estimate the state of the exosystem. In particular, an auxiliary variable is introduced in the distributed estimation algorithm to construct a data matrix, which is used to learn the system matrix of the exosystem for each subsystem. In addition, by resetting the state of the estimator and by using the identified matrix to update the estimator, all subsystems can reconstruct the state of the exosystem at an initial period of time, which is used for the neighbor subsystem to learn the system matrix of the exosystem. Based on the designed estimator, a novel distributed fault-tolerant controller is developed. Compared with the existing cooperative output regulation results, the system matrix of the exosystem considered in this article is unknown for all subsystems. Finally, a simulation example is provided to show the effectiveness of the obtained new design techniques.
机译:在本文中,提出了一种新的分布式学习控制方法,以解决具有执行器故障的线性多轴系统的协同容错输出调节问题。首先,提出了一种具有在线学习机制的分布式估计算法来识别外部系统的系统矩阵并估计外部系统的状态。特别地,在分布式估计算法中引入辅助变量以构造数据矩阵,其用于学习每个子系统的Exosystem的系统矩阵。另外,通过重置估计器的状态并通过使用所识别的矩阵来更新估计器来,所有子系统都可以在初始时间段内重建外部系统的状态,该初始时间段用于邻居子系统学习系统矩阵Exosystem。基于设计估计器,开发了一种新型分布式容错控制器。与现有的合作输出调节结果相比,本文中考虑的Exosystem的系统矩阵对于所有子系统都是未知的。最后,提供了一种模拟示例以显示所获得的新设计技术的有效性。

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