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Iterative learning control of nonlinear non-minimum phase systems and its application to system and model inversion

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3 Author(s)
Markusson, O. ; Dept. of Signals, Sensors, & Syst., R. Inst. of Technol., Stockholm, Sweden ; Hjalmarsson, H. ; Norrlof, M.

We present a model based method for reference tracking in the iterative learning control (ILC) framework. The method can be applied to nonlinear, possibly non-minimum phase, systems. The idea is to use the inverse of a linearized model in the ILC update. In the non-minimum phase case, the batch property of ILC is explored by means of non-causal filtering. Apart from reference tracking, this method is useful for system and model inversion-a problem that arises in many disciplines where nonlinear systems and models are involved, e.g. maximum likelihood identification and input design for identification for control. The method is illustrated on a numerical example

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Decision and Control, 2001. Proceedings of the 40th IEEE Conference on  (Volume:5 )

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