Adaptive neural design of fixed-time controllers for MIMO systems with nonlinear static and dynamic interactions

Autor:	C. L. Philip Chen, Kaixin Lu, Yun Zhang, Zhi Liu
Rok vydání:	2021
Předmět:	Lyapunov function Computer science Cognitive Neuroscience MIMO Tracking (particle physics) Stability (probability) Computer Science Applications symbols.namesake Nonlinear system Artificial Intelligence Fixed time Control theory symbols Analysis method Mimo systems
Zdroj:	Neurocomputing. 457:293-305
ISSN:	0925-2312
DOI:	10.1016/j.neucom.2021.06.060
Popis:	Existing fixed-time adaptive neural controllers for uncertain multi-input/multi-output (MIMO) systems with unknown nonlinear interactions only ensure practical fixed-time stabilization or require extra assumptions on system nonlinear functions. To remove these limitations and improve the result to fixed-time stabilization, a dynamic switched Lyapunov function candidate is newly proposed, based on which a novel direct adaptive neural strategy is developed to design fixed-time stable controllers for MIMO systems. To overcome the difficulty in establishing fixed-time stability in the presence of unknown interactions, a two-step Lyapunov function analysis method is proposed to prove that the tracking errors asymptotically converge to preassigned values within a fixed time. Simulation studies substantiate the methods developed.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::e8d20174abb6770a3317e96c1db52fa2 https://doi.org/10.1016/j.neucom.2021.06.060 Zobrazit plný text záznamu Full Text from ScienceDirect