Abstract
This paper focuses on fault detection filter (FDF) design for continuous-time nonlinear Markovian jump systems (NMJSs) with mode-dependent delay and time-varying transition probabilities (TPs). By using a novel Lyapunov-Krasovskii function and based on convex polyhedron technique, a new FDF, as the residual generator, is constructed to guarantee the mean-square exponential stability and a prescribed level of disturbance attenuation for admissible perturbations of NMJSs. Finally, the numerical simulation is carried out to demonstrate the effectiveness of our method.
Highlights
1 Introduction Subject to the random abrupt variations, Markovian jump systems (MJSs) are assumed to be a framework to model dynamic systems, and they can be found in economic systems, communication systems, robot manipulator systems and so on
Many efforts have been devoted to MJSs, which can be possibly used in the field of system stability [ – ], system control [ – ] and filtering [ – ]
The fault detection method can be divided into three groups
Summary
Subject to the random abrupt variations, Markovian jump systems (MJSs) are assumed to be a framework to model dynamic systems, and they can be found in economic systems, communication systems, robot manipulator systems and so on. In the framework of fault detection, a threshold on residual signals is set. Up to now many results on fault detection of MJSs have been published, see [ – ] and the references therein. The fault detection method can be divided into three groups. In [ ], a filter is used to generate the residual signals to detect the fault. Time delays are mode-dependent sometimes, and usually the existence of nonlinear terms makes the real fault detection problem more complicated. Wang et al Advances in Difference Equations (2017) 2017:262 detection for continuous-time nonlinear MJSs (NMJSs) with mode-dependent delay and time-varying TPs have been seldom carried out up to now, which motivates this paper.
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