Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 30, Issue 9, Pages 2625-2636Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2018.2886023
Keywords
Adaptive control; barrier Lyapunov functions (BLFs); neural networks (NNs); nonlinear time-delayed systems
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Funding
- National Natural Science Foundation of China [61803189, 61622303, 61603164, 61773188, 61803190]
- Program for Liaoning Innovative Research Team in University [LT2016006]
- Fundamental Research Funds for the Universities of Liaoning Province [JZL201715402]
- Program for Distinguished Professor of Liaoning Province
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This paper proposes an adaptive neural control method for a class of nonlinear time-varying delayed systems with time-varying full-state constraints. To address the problems of the time-varying full-state constraints and time-varying delays in a unified framework, an adaptive neural control method is investigated for the first time. The problems of time delay and constraint are the main factors of limiting the system performance severely and even cause system instability. The effect of unknown time-varying delays is eliminated by using appropriate Lyapunov-Krasovskii functionals. In addition, the constant constraint is the only special case of time-varying constraint which leads to more complex and difficult tasks. To guarantee the full state always within the time-varying constrained interval, the time-varying asymmetric barrier Lyapunov function is employed. Finally, two simulation examples are given to confirm the effectiveness of the presented control scheme.
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