On-line switched robust fault detection framework for switched systems with unknown inputs

This paper concerns an on-line robust fault detection (RFD) for continuous-time switched linear systems with unknown disturbances and noises. Owing to a novel event-triggered mechanism, the current mode-dependent event detector receives the sampled information of the unknown synchronous switching signal at each sampling instant; the active mode is updated. Thus, the on-line switched unknown input observer immediately estimates the system states. Using the average dwell-time (ADT) approach, new linear matrix inequalities (LMIs) are determined through an online switched quadratic Lyapunov function ensuring the globally uniformly asymptotically system stability (GUAS). So, we establish less conservative stability criteria, while the ADT switching mechanism is formulated to ensure that the estimation error converges by determining a minimum average dwell time for the switching signal. The designed robust fault detection framework is proper to find out all kinds of unknown faults (affected by each current mode), in real-time, via online residual indicators with adaptive thresholds that are robust to the deterministic input disturbances, as well as the impact of the stochastic disturbances is reduced regarding an <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mrow> <mml:mi>H</mml:mi> <mml:mi>∞</mml:mi> </mml:mrow> </mml:math> performance with on-line switched index. Simulation results on a DC-DC buck converter are given to underline the online applicability of the suggested approach. A comparative study of the RFD scheme concerning existing works is provided, where our results are shown to be less conservative and more valid.