Set-based fault detection and isolation for detectable linear parameter-varying systems

Abstract

In the context of fault detection and isolation of Linear Parameter-Varying (LPV) systems, a challenging task appears when the dynamics and the available measurements render the model unobservable, which invalidates the use of standard Set-Valued Observers (SVOs). Two results are obtained in this paper, namely: using a left-coprime factorization, one can achieve set-valued estimates with ultimately bounded hyper-volume and convergence dependent on the slowest unobservable mode; and, by rewriting the SVO equations and taking advantage of a coprime factorization, it is possible to have a low-complexity fault detection and isolation method. Performance is assessed through simulation, illustrating, in particular, the detection time for various types of faults.