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Adaptive neural control for a class of stochastic non-strict-feedback nonlinear systems with time-delay.
- Source :
-
Neurocomputing . Nov2016, Vol. 214, p750-757. 8p. - Publication Year :
- 2016
-
Abstract
- This paper addresses adaptive neural control for a class of non-strict-feedback stochastic nonlinear systems with time delays. An important structural property of radial basis function (RBF) neural networks (NNs) is introduced to overcome the design difficulty from the non-strict-feedback structure. The Lyapunov–Krasovskii functional is used for control design and stability analysis. Further, a backstepping-based adaptive neural control strategy is proposed. The suggested adaptive neural controller guarantees that all the closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges to a small neighborhood of the origin. Simulation results demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09252312
- Volume :
- 214
- Database :
- Academic Search Index
- Journal :
- Neurocomputing
- Publication Type :
- Academic Journal
- Accession number :
- 118813588
- Full Text :
- https://doi.org/10.1016/j.neucom.2016.06.060