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Regular papers

Prescribed performance control for MIMO stochastic discrete-time nonlinear systems in a strict-feedback form using a set of noisy measurements

Pages 689-703 | Received 27 Mar 2021, Accepted 15 Aug 2021, Published online: 22 Sep 2021
 

Abstract

This paper presents a prescribed performance control for MIMO stochastic discrete-time nonlinear systems in a strict-feedback form using a set of noisy measurements. The prescribed performance control is proposed as follows. Transforming the un-constrained states into the constrained states, the proposed prescribed performance control with state constraints is designed based on the approach of backstepping control and the Lyapunov function without using approximate approaches. The nonlinear uncertainty is approximated as the fuzzy logic system based on the simplified extended single input rule modules to reduce the number of the fuzzy IF–THEN rules. The estimator to take the estimates for the unmeasurable states and the adjustable parameters is in a simplified structure designed. The effectiveness of the proposed approach is indicated through the simulation experiment of a simple numerical system.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Toshio Yoshimura

Toshio Yoshimura is Professor Emeritus of the University of Tokushima, Japan. He was graduated from Faculty of Engineering, the University of Tokushima in 1963, and received his PhD degree from Kyoto University in 1974. He has been Professor from 1982 through 2006 in Faculty of Engineering, the University of Tokushima, and has been appointed as Dean in the faculty from 2001 through 2003. His research includes the adaptive control, fuzzy logic control, sliding mode control, and backstepping control for uncertain nonlinear systems. He received the Paper Awards in 1975 and 2017 from JSME, and was recommended as the Honorary Member in 2006 from the JSME.

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