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Abstract
ABSTRACTFeedforward control enables high performance of a motion system. Recently, algorithms have been proposed that eliminate bias errors in tuning the parameters of a feedforward controller. The aim of this paper is to develop a new algorithm that combines unbiased parameter estimates with optimal accuracy in terms of variance. A simulation study is presented to illustrate the poor accuracy properties of pre-existing algorithms compared to the proposed approach. Experimental results obtained on an industrial nanopositioning system confirm the practical relevance of the proposed method.
Highlights
Challenging requirements on positioning accuracy often necessitate the use of feedforward control for motion systems, since feedforward can effectively compensate for the error induced by known, repeating disturbances
A new algorithm is proposed for iterative feedforward control based on instrumental variables
The key advantage of the proposed algorithm is that it achieves optimal accuracy in terms of variance, in contrast to existing approaches, which are shown to be non-optimal
Summary
Challenging requirements on positioning accuracy often necessitate the use of feedforward control for motion systems, since feedforward can effectively compensate for the error induced by known, repeating disturbances. Traditional approaches that can potentially achieve these requirements on positioning accuracy include iterative learning control (ILC) (Bristow, Tharayil, & Alleyne, 2006; Gorinevsky, 2002) and model-based feedforward (Butterworth et al, 2012; Zhong, Pao, & de Callafon, 2012). ILC enables superior performance with respect to model-based feedforward for a specific task by compensating for all repetitive disturbances. In contrast to ILC, modelbased feedforward results in moderate performance for a class of reference signals instead of only one specific reference (Butterworth et al, 2012). Note that the performance for model-based feedforward is highly dependent on the model quality of the parametric model of the system and the accuracy of model-inversion (Devasia, 2002)
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