Abstract
The present work investigates a fault detection problem using a gain-scheduled filter for discrete-time Linear Parameter Varying systems. We assume that we cannot directly measure the scheduling parameter but, instead, it is estimated. On the one hand, this assumption imposes the challenge that the fault detection filter should perform properly even when using an inexact parameter. On the other, it avoids the burden associated with designing a complex estimation process for this parameter. We propose three design approaches: the ${\mathcal {H}_{2}}$ , ${\mathcal {H}_{\infty }}$ , and mixed ${\mathcal {H}_{2}} / {\mathcal {H}_{\infty }}$ gain-scheduled Fault Detection Filters designed via Linear Matrix Inequalities. We also provide numerical simulations to illustrate the applicability and performance of the proposed novel methods.
Highlights
T HE occurrence of faults is inherent in any complex engineering systems such as, for instance, in multitude of sensor and actuator systems used to drive and operate high degree-of-freedom electromechanical mechanisms
(a) Evaluation Function for fault detection filters (FDF) designed via Theorem 1 subjected to Fault 1
(m) Evaluation Function for FDF designed via Theorem 1 for the nominal conditions without fault
Summary
T HE occurrence of faults is inherent in any complex engineering systems such as, for instance, in multitude of sensor and actuator systems used to drive and operate high degree-of-freedom electromechanical mechanisms. The presence of these faults may, among others, lead to significant performance degradation and can yield to unsafe operations [1]. In [5], the authors present a comparison between the model-based and data-driven approaches considering an Unmanned Aerial Vehicle. In cases where the system is only partially observable or where it requires further implementation of sensors, controllers, or security measurements, it is difficult or impossible to gather enough data to implement a data-driven approach, but it is still possible to obtain a model to describe the system
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