Sliding mode observers for fault detection of uncertain LPV systems with imperfect scheduling parameter knowledge

This paper presents a fault detection scheme for linear parameter varying (LPV) systems with uncertain or imperfectly measured scheduling parameters, using sliding mode observers (SMOs). In most LPV systems, it is assumed that the scheduling parameters are exactly known, but due to noise or faulty sensors, it is sometimes not possible to know the scheduling parameters perfectly, and a design based on nominal scheduling parameters cannot be guaranteed to work well in this situation. In this paper a SMO is proposed to reconstruct actuator faults in the situation where the scheduling parameters are imperfectly known. In this paper the observer gains are obtained from a linear matrix inequality optimisation and a rigorous error analysis. The efficacy of the proposed approach is demonstrated on a RECONFIGURE actuator fault benchmark problem.