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Experience-based iterative learning controllers for robotic systems
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DOI：10.1023/A:1022399105710.png)
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Abstract
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An experience based iterative learning controller is proposed for a general class of robotic systems. Experience of the iterative learning controller is stored in the memory in terms of input output data and later used for the prediction of the initial control input for a new desired trajectory. It is proved in this paper that using this approach we can reduce the number of iterations to achieve a certain user defined tracking accuracy. This approach is very general and applicable to all kinds of existing iterative learning control schemes. Numerical illustrations showed the effectiveness of the proposed method.
Keywords:
iterative learning
control
trajectory tracking
convergences
robotic systems
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