Title of Paper:Probability-constrained tracking control for a class of time-varying nonlinear stochastic systems

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Affiliation of Author(s):自动化学院

Journal:JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS

Key Words:INFINITY FILTER DESIGN NETWORKS NOISES DELAY

Abstract:This paper is concerned with the probability-constrained tracking control problem for a class of time-varying systems with stochastic nonlinearities, stochastic noises and successively packet loss. The main purpose of this paper is to design a time-varying observer and tracking controller such that (1) the probabilities of both the estimation error and tracking error confined to given ellipsoidal sets are larger than prescribed constants, and (2) the ellipsoids are minimized in the sense of matrix norm at each time point. By using a stochastic analysis method, the probability constrained tracking control problem is solved and sufficient conditions are obtained in terms of recursive linear matrix inequalities. A recursive optimization algorithm is developed to design the observer and tracking controller such that not only the addressed probability constrained aim is satisfied, but also the ellipsoidal sets are minimized. At last, a simulation example is given to illustrate the effectiveness and applicability of the developed approach. (C) 2018 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

ISSN No.:0016-0032

Translation or Not:no

Date of Publication:2018-03-01

Co-author:Tian, Engang

Correspondence Author:lcs