Intermediate observers based robust fault estimation for multi-agent systems under communication constraints via switching scheme.

This paper addresses the problem of robust fault estimation for multi-agent systems (MASs) under communication constraints. Taking into account the possible data packet loss (DPL) in the information interaction of each subsystem, MASs are remodelled as switching systems by introducing a variable sampling strategy. Then, using the local information among agents, a novel intermediate observer design method based on switching scheme is proposed to estimate faults of MASs.

Application of multi-SVM classifier and hybrid GSAPSO algorithm for fault diagnosis of electrical machine drive system.

A method, being based on multi-class support vector machine (SVM) classifier and hybrid particle swarm optimization (PSO) and gravity search algorithm (GSA), is presented to diagnose the faults in electrical motor drive system. In this method, the global search ability of PSO and the local search ability of GSA are integrated to combine the advantages of PSO and GSA, and the multi-class SVM classifier is optimized by the hybrid GSAPSO algorithm to improve classification performance. To test the presented method, a series of simulation and experiment are studied.