Feedforward motion control: from batch-to-batch learning to online parameter estimation
conference paper
Feedforward control is essential in highperformance motion control. The aim of this paper is to develop a unified framework for automatic feedforward optimization from both batch-wise data sets as well as real-time data. A statistical analysis is employed to analyze the effect of noise, i.e., an iteration varying disturbance, on feedforward controller performance. This provides new insights, both potential advantages as well as possible hazards of real-time estimation are considered. Finally, a case study confirms and illustrates the results.
TNO Identifier
869400
ISSN
7431619
Publisher
Institute of Electrical and Electronics Engineers IEEE
Article nr.
8814481
Source title
Proceedings of the American Control Conference, ACC 2019, Philadelphia, PA, USA, 10-12 July 2019
Collation
6 p.
Pages
947-952
Files
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