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Norm-Optimal Iterative Learning Control Applied to Gantry Robots for Automation Applications

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5 Author(s)
James D. Ratcliffe ; Sch. of Electron. & Comput. Sci., Southampton Univ. ; Paul L. Lewin ; Eric Rogers ; Jari J. Htnen
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This paper is concerned with the practical implementation of the norm-optimal iterative learning control (NOILC) algorithm. Here, the complexity of this algorithm is first considered with respect to real-time control applications, and a new modified version, fast norm-optimal ILC (F-NOILC), is derived for this application, which potentially allows implementation with a sampling rate three times faster that the original algorithm. A performance index is used to assess the experimental results obtained from applying F-NOILC to an industrial gantry robot system and, in particular, the effects of varying the parameters in the cost function, which is at the heart of the norm-optimal approach

Published in:

IEEE Transactions on Robotics  (Volume:22 ,  Issue: 6 )