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. * The research leading to these results has received funding from the European Union H2020 program under grant agreement n. 637095 (Four-ByThree) and ECSEL-2016-1 under grant agreement n. 737453 (I-MECH).
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Iterative learning control (ILC) enables a perfect compensation for systems that perform the same task over and over again. The aim of this paper is to demonstrate practical applicability of two various state-of-the-art ILC algorithms to point-to-point positioning systems. A simple Frequency domain ILC approach is exploited focusing on systems with exactly repeating motion tasks. Furthermore, flexible ILC is employed to enable learning also for non-repeating tasks. Particular steps providing a seamless transfer from theory and algorithms to practical implementation in a real-time environment by means of industrial-grade SW and HW are given. They may serve as a practical example of a workflow suitable for a wide range of motion control applications. Potential benefits of the learningtype control in comparison with conventional feedback and feedforward control are discussed as well.
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