Decentralised disturbance-observer-based adaptive tracking in the presence of unmatched nonlinear time-delayed interactions and disturbances

ABSTRACT

This paper proposes an approximation-based nonlinear disturbance observer (NDO) approach for decentralised adaptive tracking of uncertain interconnected pure-feedback nonlinear systems with unmatched time-delayed nonlinear interactions and external disturbances. Compared with the existing approximation-based NDO approach for uncertain interconnected nonlinear systems where the centralised design framework was proposed, the main contribution of this paper is to develop a decentralised and memoryless NDO-based adaptive control scheme in the presence of unknown time-varying delayed interactions and disturbances unmatched in the control inputs. The recursive design methodology is derived to construct the decentralised NDO and controller where the function approximators used in the decentralised NDO are employed to design the decentralised adaptive controller. From the Lyapunov stability theorem using Lyapunov--Krasovskii functionals, it is shown that all signals of the closed-loop system are semi-globally uniformly ultimately bounded and the tracking errors converge to an adjustable neighbourhood of the origin.

KEYWORDS:

• Decentralised tracking
• nonlinear disturbance observer (NDO)
• unknown time-varying delayed interactions
• unmatched external disturbances

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program [IITP-2017-2014-0-00636] supervised by the IITP (Institute for Information & communications Technology Promotion); the Human Resources Development of the Korea Institute of Energy Technology Evaluation and Planning through the Korea government Ministry of Trade, Industry, and Energy [grant number 20154030200860]; and the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education [grant number NRF-2016R1D1A1B03931312].

Notes on contributors

Hyoung Oh Kim

Hyoung Oh Kim received his B.S. degree from the School of Electrical and Electronics Engineering, Chung-Ang University, Seoul, Korea, in 2016, where he is currently pursuing the Master degree with the Department of Electrical and Electronic Engineering. His current research interests include nonlinear adaptive control, nonlinear disturbance observer, and intelligent control using neural networks.

Sung Jin Yoo

Sung Jin Yoo received his B.S., M.S., and Ph.D. degrees in Electrical and Electronic Engineering from Yonsei University, Seoul, South Korea, in 2003, 2005, and 2009 respectively. He has been a post-doctoral researcher in the Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Illinois, from 2009 to 2010. Since 2011, he has been with the School of Electrical and Electronics Engineering, Chung-Ang University, Seoul, South Korea, where he is currently an associate professor. His research interests include nonlinear adaptive control, decentralized control, distributed control, fault tolerant control, and neural networks theories, and their applications to robotic, flight, nonlinear time-delay systems, large-scale systems, and multi-agent systems.