Neural network adaptive compensation control based on the backstepping method for nonlinear systems with time-varying delays

Libin Ma and Mao Wang

International Journal of Systems Science, 2023, vol. 54, issue 6, 1196-1212

Abstract: Control of nonlinear systems with time-varying delays is a common problem, particularly when the time-varying delays are unknown. This paper presents an adaptive compensation control method based on multiple neural networks. Firstly, considering the nonlinear systems with time-varying delays, a neural network method is adopted to approximate the unknown nonlinear structure with time-varying delays. On this basis, a backstepping controller is designed. Considering the unknown time-varying delays, the control effect of the neural network controller based on the backstepping method is not optimal. To deal with the problem, a double-layer adaptive compensator for the neural network is proposed. Furthermore, for the neural network adaptive compensation controller based on the backstepping method, the stability is also proved by a Lyapunov method. Finally, simulation results demonstrate the effectiveness of the scheme, especially when the nonlinear systems have an unknown time-varying delay.

Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00207721.2023.2169056 (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:54:y:2023:i:6:p:1196-1212

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

DOI: 10.1080/00207721.2023.2169056

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 ().