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Removing Feasibility Conditions on Adaptive Neural Tracking Control of Nonlinear Time-Delay Systems With Time-Varying Powers, Input, and Full-State Constraints.
- Source :
- IEEE Transactions on Cybernetics; Apr2022, Vol. 52 Issue 4, p2553-2564, 12p
- Publication Year :
- 2022
-
Abstract
- This article investigates the tracking control for input and full-state-constrained nonlinear time-delay systems with unknown time-varying powers, whose nonlinearities do not impose any growth assumption. By utilizing the auxiliary control signal and nonlinear state-dependent transformation (NSDT) to counteract the effect of input saturation and cope with full-state constraints, respectively, and then introducing lower and higher powers and Lyapunov–Krasovskii (L–K) functionals in control design together with the adaptive neural-networks (NNs) method, an adaptive neural tracking control design is provided without feasibility conditions. It is proved that NNs approximation is valid, all the closed-loop signals are semiglobally bounded, and input and full-state constraints are not violated. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 21682267
- Volume :
- 52
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Cybernetics
- Publication Type :
- Academic Journal
- Accession number :
- 156272921
- Full Text :
- https://doi.org/10.1109/TCYB.2020.3003327