Nonstationary [formula omitted] filtering for Markov switching repeated scalar nonlinear systems with randomly occurring nonlinearities

Abstract

This paper is concerned with nonstationary l2−l∞ filtering for Markov switching repeated scalar nonlinear systems (MSRSNSs) with randomly occurring nonlinearities (RONs), where measurement output is modeled by a mode-dependent random variable that satisfying Bernouli distribution. The new relationship are proposed to depict multiple mutually independent Markov chains between original MMSRSNSs and nonstationary filters. By constructing a proper Lyapnov function, the MSRSNSs is stochastically stable with l2−l∞ performance level is guaranteed. Accordingly, the nonstationary filters are designed, where filters are characterised by a two-layer structure. The paper provides a numerical example verifying the efficacy of established technique.