Adaptive Backstepping Control for a Class of Nonlinear Uncertain Systems using Fuzzy Neural Networks

2007 ◽  
Author(s):  
Ching-Hung Lee ◽  
Bo-Ren Chung ◽  
Fu-Kai Chang ◽  
Sheng-Kai Chang
Keyword(s):  
Neural Networks ◽  
Uncertain Systems ◽  
Fuzzy Neural Networks ◽  
Backstepping Control ◽  
Adaptive Backstepping ◽  
Adaptive Backstepping Control ◽  
Fuzzy Neural ◽  
Nonlinear Uncertain Systems

Adaptive Backstepping Control Based upon DRFNN for RCRBV

2011 ◽  
Vol 403-408 ◽  
pp. 5082-5087
Author(s):  
Ping Jun Zhang ◽  
Xin Hua Jiang
Keyword(s):  
Control Method ◽  
Mathematic Model ◽  
Fuzzy Neural Networks ◽  
Backstepping Control ◽  
Lyapunov Theory ◽  
Uncertain Parameters ◽  
Adaptive Backstepping ◽  
Adaptive Backstepping Control ◽  
Fuzzy Neural ◽  
Uncertainty Parameters

For the nonliear and uncertainty parameters of the running driving component of Resonant cement-road breaking vehicle (RCRBV), the mathematic model of the speed Control is established, a adaptive backstepping control method based upon the dynamic recurrent fuzzy neural networks (DRFNN) is presented. The adaptive backstepping controlling arithmetic is designed firstly in transportational status without regard to the uncertain parameters. The convergence based on Lyapunov theory for the closed loop system is also analysised. secondly, the uncertain parameters of the Electro-hydraulic propotional system which affect the running speed controlling performances are defined as items to be estimated by DRFNN in breaking status to meet the high precision and stability requires, the parameter adjustment law is given based upon DRFNN. Finally, the results of the simulation show that the scheme is robust with respect to plant parameter variations and load disturbances.

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