Adaptive neural output-feedback control for a class of output-constrained switched stochastic nonlinear systems

Fei Shen, Xinjun Wang and Xinghui Yin

International Journal of Systems Science, 2021, vol. 52, issue 16, 3526-3538

Abstract: In this paper, a neural network-based adaptive output-feedback control problem is investigated for a class of output-constrained switched stochastic nonlinear systems. By introducing nonlinear mapping, the asymmetric and symmetric output constrained stochastic nonlinear system is transformed into a new system without any constraint. It is the first time that a switching system is used to convert symmetric and asymmetric output constraints in the same system. An adaptive neural output-feedback controller is developed based on the backstepping technique. A state observer is designed to estimate the unmeasurable system state signals. An adaptive controller is designed to ensure that the output tracking error converges to a small region of the origin. The control scheme ensures that all signals in the closed-loop systems are semi-global uniformly ultimately bounded. Results of simulation cases are presented to prove the effectiveness of the theoretical analysis.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00207721.2021.1931728 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tsysxx:v:52:y:2021:i:16:p:3526-3538

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TSYS20

DOI: 10.1080/00207721.2021.1931728

Access Statistics for this article

International Journal of Systems Science is currently edited by Visakan Kadirkamanathan

More articles in International Journal of Systems Science from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().