Adaptive output-feedback tracking control for a class of nonlinear systems with input saturation: a multi-dimensional Taylor network-based approach

Abstract

For the nonlinear systems with input saturation constraint, a novel MTN-based output-feedback control strategy is developed via backstepping in this paper. Firstly, an MTN-based nonlinear state observer is designed to estimate the unmeasured states. Secondly, multi-dimensional Taylor networks (MTNs) are used to deal with the unknown nonlinear functions, based on this, the procedure of the adaptive MTN output-feedback controller design is developed by combining backstepping approach and dynamic surface control technique, and then the stability of the closed-loop system is proved based on the principle of Lyapunov stability theory. Finally, the theoretical results of this paper are demonstrated by two examples.

KEYWORDS:

• Multi-dimensional Taylor network
• adaptive control
• nonlinear systems
• input constraints
• backstepping

Disclosure statement

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

Additional information

Notes on contributors

Yu-Qun Han

Yu-Qun Han received the B.S. degree in mathematics and applied mathematics and the M.S. degree in applied mathematics from Qingdao University of Science and Technology, Qingdao, China, in 2010 and 2013, respectively, and the Ph.D. degree in control theory and control engineering form Southeast University, Nanjing, China, in 2018. He has been with the School of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, China, since December 2018. His current research interests include nonlinear system control, stochastic nonlinear system control, adaptive control, multi-dimensional Taylor networks and neural networks.