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Cooperative Fault Diagnosis for Uncertain Nonlinear Multiagent Systems Based on Adaptive Distributed Fuzzy Estimators
IEEE Transactions on Cybernetics ( IF 11.8 ) Pub Date : 2020-04-01 , DOI: 10.1109/tcyb.2018.2877101
Hong-Jun Ma , Linxing Xu

This paper presents a cooperative fault diagnosis scheme for a class of uncertain nonlinear multiagent systems component and sensor faults in individual agents. Since the faulty system affects the healthy systems through interconnections, for each agent an estimator is designed to collect neighboring output estimations errors to consider its faulty effects on others, when computing its estimations for local state and faulty parameters. A new structure of distributed estimators is proposed by filtering regressor signals and sharing them among agents. Then, the sharings of signals are planned by properly constructing auxiliary graphs for undirected and directed networks. Two conditions are given to preselect estimators parameters for the convergences of the estimation errors. Unlike the existing results dealing with one common parameter with full state measurement and only for undirected graphs, this paper presents an output measurement-based approach for multiple parameters in undirected/directed networks. It shows that for the faults not providing persistent excitation in a signal agent, it is possible to estimate the faults exactly if the they excite all agents persistently. A simulation example of a group of single-link flexible-joint robots is given to verify the effectiveness of the proposed method.

中文翻译:

基于自适应分布式模糊估计器的不确定非线性多Agent系统协同故障诊断。

针对一类不确定的非线性多智能体系统组件和单个智能体中的传感器故障,提出了一种协作式故障诊断方案。由于故障系统通过互连影响健康系统,因此,对于每个代理,设计一个估算器,以便在计算其本地状态和故障参数的估算值时,收集相邻的输出估算错误,以考虑其对其他产品的故障影响。通过过滤回归信号并在代理之间共享它们,提出了一种分布式估计器的新结构。然后,通过正确构造无向和有向网络的辅助图来计划信号共享。为估计误差的收敛,给预选估计器参数提供了两个条件。与现有的处理全状态测量的通用参数且仅针对无向图的结果不同,本文提出了一种基于输出测量的无向/有向网络中多个参数的方法。它表明,对于没有在信号代理中提供持续激励的故障,如果它们持续激励所有代理,则可以准确估计故障。以一组单链柔性关节机器人为例,验证了所提方法的有效性。如果故障持续激发所有代理,则可以准确估计故障。以一组单链柔性关节机器人为例,验证了所提方法的有效性。如果故障持续激发所有代理,则可以准确估计故障。以一组单链柔性关节机器人为例,验证了所提方法的有效性。
更新日期:2020-04-01
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