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arXiv · 2606.03533

Recursive Learning of Feedforward and Compliance Compensation Parameters for Precision Motion Systems

M. Wind·J. Pierssens·R. Beerens·V. Dolk·T. van Keulen
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Abstract

To meet the stringent requirements of future motion systems exhibiting time-varying and/or position-dependent behavior, online data must be leveraged to improve control performance. This paper presents a recursive algorithm for simultaneous learning of feedforward and compliance compensation parameters. A multivariate regression formulation is proposed that jointly estimates friction, mass, jerk, and compliance compensation parameters while mitigating parameter coupling. Experimental results on a high-tech semiconductor metrology and inspection system demonstrate an order-of-magnitude improvement in servo performance.

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M. Wind, J. Pierssens, R. Beerens, V. Dolk, T. van Keulen. 2026-06-02. Recursive Learning of Feedforward and Compliance Compensation Parameters for Precision Motion Systems. https://arxiv.org/abs/2606.03533

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