期刊
NEUROCOMPUTING
卷 330, 期 -, 页码 490-503出版社
ELSEVIER
DOI: 10.1016/j.neucom.2018.11.029
关键词
RBF neural network; Dynamic surface control; Nonlinear System; Adaptive control law
资金
- National Basic Research Program of China [61873305, U1830207, 51502338, 61503064, 61671109, 2018JY0410]
- Open Foundation of Hypervelocity Impact [20181102]
- Fundamental Research Funds for the Central University
This paper develops an adaptive backstepping dynamic surface control method with RBF Neural Network for a class of nonlinear system under extra disturbances. The considered RBF Neural Network based on adaptive control is applied to approximate the unknown smooth function arbitrarily. The explosion of the complexity is eliminated by utilizing the dynamic surface control technique. The Lyapunov function is employed to verify the globally asymptotically stability of the control nonlinear system. Four examples were given to show that the novel control method can not only tracking the expected trajectory very well but also has a better approximation capability for various complex unknown smooth function under disturbances. (C) 2018 Elsevier B.V. All rights reserved.
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