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

arXiv CS Wednesday 03 June 2026, 04:00 UTC By M. Wind, J. Pierssens, R. Beerens, V. Dolk, T. van Keulen 1 min read
Key Points

arXiv:2606.03533v1 Announce Type: new 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...

arXiv:2606.03533v1 Announce Type: new 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.
Originally published by arXiv CS Read original →

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