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

Authors:
J. D. Ratcliffe;P. L. Lewin;E. Rogers;J. J. Hatonen;D. H. Owens
Affiliations:
Sch. of Electron. & Comput. Sci., Southampton Univ.;-;-;-;-
Venue:
IEEE Transactions on Robotics
Year:
2006

Citing 0
Cited 4

Strong practical stability and stabilization of discrete linear repetitive processes

Multidimensional Systems and Signal Processing
Repetitive process based iterative learning control designed by LMIs and experimentally verified on a gantry robot

ACC'09 Proceedings of the 2009 conference on American Control Conference
On the Development of SCILAB Compatible Software for the Analysis and Control of Repetitive Processes

International Journal of Applied Mathematics and Computer Science - Selected Problems of Computer Science and Control
A computationally efficient norm optimal iterative learning control approach for LTV systems

Automatica (Journal of IFAC)

Quantified Score

Hi-index	0.00

Visualization

Abstract

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