Application of RBF neural networks in robust adaptive DSC design of nonlinear systems

2017 ◽  
Author(s):  
Nahid Abasi ◽  
Marzieh Kamali ◽  
Maryam Zekri
Keyword(s):  
Neural Networks ◽  
Nonlinear Systems ◽  
Rbf Neural Networks ◽  
Robust Adaptive

Robust adaptive tracking for nonlinear systems using dynamic neural networks

1999 ◽  
Vol 32 (2) ◽  
pp. 4476-4481
Author(s):  
Dai Qionghai ◽  
Wu Hongwei ◽  
Sun Fuxin ◽  
Li Yanda ◽  
Wang Wei ◽  
...  
Keyword(s):  
Neural Networks ◽  
Nonlinear Systems ◽  
Dynamic Neural Networks ◽  
Adaptive Tracking ◽  
Robust Adaptive

Adaptive RBF neural-networks control for a class of time-delay nonlinear systems

2008 ◽  
Vol 71 (16-18) ◽  
pp. 3617-3624 ◽  
Author(s):  
Qing Zhu ◽  
Shumin Fei ◽  
Tianping Zhang ◽  
Tao Li
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Nonlinear Systems ◽  
Rbf Neural Networks

scholarly journals Adaptive Neural Control for a Class of Outputs Time-Delay Nonlinear Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Ruliang Wang ◽  
Jie Li
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Nonlinear Systems ◽  
Neural Control ◽  
Closed Loop ◽  
Continuous Functions ◽  
Rbf Neural Networks ◽  
Adaptive Neural Control ◽  
Control Scheme ◽  
Simulation Results

This paper considers an adaptive neural control for a class of outputs time-delay nonlinear systems with perturbed or no. Based on RBF neural networks, the radius basis function (RBF) neural networks is employed to estimate the unknown continuous functions. The proposed control guarantees that all closed-loop signals remain bounded. The simulation results demonstrate the effectiveness of the proposed control scheme.

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