Resilient estimation for a class of Markov jump linear systems with unideal measurements and its application to robot arm systems

Abstract

In this paper, the resilient H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> filtering problem for a class of discrete-time Markov jump systems with unideal measurements is investigated. The unideal measurements contain both quantization and missing measurements simultaneously, which occur randomly satisfying two mutually independent Bernoulli distribute white sequences. A unified model is used to describe the unideal measurements phenomena, and a norm-bounded additive gain perturbation is introduced to model the resilient filter. A mode-dependent full-order filter is designed such that the filtering error system is stochastically stable with an ensured H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance index. An application on a single-link robot arm is provided to verify the theoretical results.