Backstepping Adaptive Iterative Learning Control for Robotic Systems

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 Lyapuno...

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Bibliographic Details
Published inApplied Mechanics and Materials Vol. 284-287; pp. 1759 - 1763
Main Authors Chien, Chiang Ju, Chuang, Chi Nan, Wang, Ying Chung
Format Journal Article
LanguageEnglish
Published Zurich Trans Tech Publications Ltd 25.01.2013
Subjects
Robotic Systems
Backstepping
Adaptive Iterative Learning Control
Fuzzy Neural Network (FNN)
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Summary: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.
Bibliography:Selected, peer reviewed papers from the Second International Conference on Engineering and Technology Innovation 2012, November 2 - 6, 2012, Kaohsiung, Taiwan, R. O. C.
ISBN:3037856122
9783037856123
ISSN:1660-9336
1662-7482
1662-7482
DOI:10.4028/www.scientific.net/AMM.284-287.1759