Active fault tolerant control design using switching linear parameter varying controllers with inexact fault‐effect parameters

Abstract

In this article, a novel active fault-tolerant control (AFTC) design method is developed by using the switching linear parameter varying (LPV) controller with inexact fault-effect parameters. The LPV controller is not only considered as a gain-scheduled controller varying with the operating points, but also an AFTC controller varying with the fault-effect parameters, which can be obtained via the fault estimator in real-time. The inexact measurement of fault-effect parameters is considered for the first time in this article. To cover large parameters variation, a class of switched LPV fault-tolerant controller is designed to work in the multiple partitioned parameters subregions. The dynamic output feedback controller model is constructed to perform the switched LPV fault-tolerant control task under the mode-dependent average dwell time constraint. By using the multiple parameter-dependent Lyapunov function approach, sufficient conditions with less conservatism are obtained, such that the corresponding closed-loop systems are globally uniformly exponentially stable and satisfy an upper bound of weighted l 2 -gain performance indexes. Finally, effectiveness and applicability of the developed approaches are validated via an active magnetic bearing system.

Control Strategies for Highly Gyroscopic Outer Rotors with Diametral Enlargement in Active Magnetic Bearings

“…To do so, the variation of the speed-dependent magnetic bearing factors k s and k i must be introduced into the control law. This could either be done by switching between different controllers with discrete feedback gains according to the rotor speed as in [39,40] or by a single controller with continuously varying feedback gains [8,23]. The latter design was chosen and will be discussed in the subsequent section.…”

Editorial: Hybrid intelligent algorithms based learning, optimization, and application to autonomic control systems, volume II

“…More recently, various intelligent methodology and hybrid control technology have been developed for autonomic control systems in the literature (see Na et al, 2019 ; Allen et al, 2022 ; Che et al, 2022 and reference therein), which also have been applied to different scenarios in practice, including aerial vehicles (Kumar and Michael, 2012 ), permanent magnet synchronous machine (Egidio et al, 2022 ), and active magnetic bearing (Che et al, 2022 ), etc. Among them, it is desired that the sophisticated hybrid intelligent algorithms can be adapted to the learning-based optimization and control design to increase the autonomous performance of equipment.…”