Fault Detection for Underactuated Manipulators Modeled by Markovian Jump Systems

This paper is concerned with the fault detection problem for underactuated manipulators based on the Markovian jump model. The purpose is to design a fault detection filter such that the filter error system is stochastically stable and the prescribed probability constraint performance can be guaranteed. The existence conditions for a fault detection filter are proposed through the stochastic analysis technique, and a new fault detection filter algorithm is employed to design the desired filter gains. In addition, the cone complementarity linearization procedure is employed to cast the filter design into a sequential minimization problem, which can be solved efficiently using existing optimization techniques. A numerical example is exploited to illustrate the effectiveness of the proposed method.