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Robust iterative learning control based on neural network for a class of uncertain robotic systems

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3 Author(s)
Yanchen Liu ; The Seventh Research Division, Beihang University (BUAA), Beijing 100083, China ; Yingmin Jia ; Zhuo Wang

This paper studies the problem of adaptive robust iterative learning control for trajectory-tracked task of a class of robotic systems with both structured and unstructured uncertainties. A composite control scheme is proposed in which the periodic uncertainties are approached by the learning controller, while the effect of non-periodic uncertainties on system performances is attenuated by the robust controller. In particular, by employing neural network the cone-bounded assumption on uncertain dynamics is removed. The simulation results are included

Published in:

2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control

Date of Conference:

4-6 Oct. 2006