An output recurrent fuzzy neural network based iterative learning control for nonlinear systems

2008 ◽  
Author(s):  
Ying-Chung Wang ◽  
Chiang-Ju Chien ◽  
Der-Tsai Lee
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Iterative Learning Control ◽  
Fuzzy Neural Network ◽  
Learning Control ◽  
Iterative Learning ◽  
Fuzzy Neural ◽  
Recurrent Fuzzy Neural Network

Backstepping Adaptive Iterative Learning Control for Robotic Systems

2013 ◽  
Vol 284-287 ◽  
pp. 1759-1763
Author(s):  
Ying Chung Wang ◽  
Chiang Ju Chien ◽  
Chi Nan Chuang
Keyword(s):  
Neural Network ◽  
Iterative Learning Control ◽  
Fuzzy Neural Network ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Robotic Systems ◽  
Adaptive Iterative Learning Control ◽  
Repetitive Tasks ◽  
Fuzzy Neural

A backstepping adaptive iterative learning control for robotic systems with repetitive tasks is proposed in this paper. The backstepping-like procedure is introduced to design the AILC. A fuzzy neural network is applied for compensation of the unknown certainty equivalent controller. Using a Lyapunov like analysis, we show that the adjustable parameters and internal signals remain bounded, the tracking error will asymptotically converge to zero as iteration goes to infinity.

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