Journal
ISA TRANSACTIONS
Volume 101, Issue -, Pages 60-68Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.isatra.2020.01.021
Keywords
Neural network; Output feedback; Full state constraints; Dynamic surface control; Stochastic nonlinear systems
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Funding
- National Natural Science Foundation of China [61673129, 61703050]
- Shandong Provincial Natural Science Foundation, China [ZR2018MF015]
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This paper presents an adaptive neural network output feedback control method for stochastic nonlinear systems with full state constraints. The barrier Lyapunov functions are used to conquer the effect of state constraints to system performance. The neural network state observer is established to estimate the unmeasured states. By using dynamic surface control technique, the explosion of complexity'' issue existing in the backstepping design is overcome. The proposed control scheme can guarantee that all signals of the system are bounded and the system output can follow the desired signal. Finally, two examples are given to verify the effectiveness of our control method. (C) 2020 ISA. Published by Elsevier Ltd. All rights reserved.
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