Nonstationary l2−l∞ filtering for Markov switching repeated scalar nonlinear systems with randomly occurring nonlinearities

Jun Cheng and Yang Zhan

Applied Mathematics and Computation, 2020, vol. 365, issue C

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.

Keywords: MSRSNSs; Bernouli distribution; Markov chains; Nonstationary filters; Randomly occurring nonlinearities (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:365:y:2020:i:c:s0096300319307064

DOI: 10.1016/j.amc.2019.124714

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