Robust model predictive control for discrete-time Takagi-Sugeno fuzzy systems with structured uncertainties and persistent disturbances

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

Detail(s)

Original languageEnglish
Article number2286635
Pages (from-to)1213-1228
Journal / PublicationIEEE Transactions on Fuzzy Systems
Volume22
Issue number5
Online published21 Oct 2013
Publication statusPublished - 1 Oct 2014

Abstract

In this paper, robust model predictive control for uncertain discrete-time Takagi-Sugeno (T-S) fuzzy systems with input constraints and persistent disturbances is considered. The robust positively invariant set for T-S fuzzy systems is investigated. Based on this result, computation of the terminal constraint set is proposed, which is of crucial importance in the robust predictive controller design. A zero-step predictive controller is discussed first, which has a time-varying terminal constraint set. The recursive feasibility and input-to-state stability can be ensured. Then, a novel controller withN-step prediction is further proposed, which can be used to dealwith the case of fixed terminal constraint set. The implementation of the N-step controller involves both online and offline computations. It is shown that a sequence of approximating robust one-step sets can be computed offline. Then, bisection searches are carried out online, as well as a constrained convex optimization problem. The N-step controller guarantees that the system state can be steered to the terminal constraint set in less than N-steps, if the initial state lies in a specific region. Simulation results are finally presented to show the effectiveness of the proposed controllers.

Research Area(s)

  • Input-to-state stability, Robust model predictive control (MPC), Takagi-Sugeno (T-S) fuzzy models

Citation Format(s)

Robust model predictive control for discrete-time Takagi-Sugeno fuzzy systems with structured uncertainties and persistent disturbances. / Yang, Weilin; Feng, Gang; Zhang, Tiejun.
In: IEEE Transactions on Fuzzy Systems, Vol. 22, No. 5, 2286635, 01.10.2014, p. 1213-1228.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review