Experimentally-efficient data-driven rational feedforward control for multivariable systems

Maurice Poot (Corresponding author), Jim Portegies, Dragan Kostic, Tom A.E. Oomen

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

Samenvatting

Feedforward control with task flexibility for MIMO systems is essential to meet the growing demands on throughput and accuracy of high-tech systems. The aim of this paper is to develop an experimentally efficient framework for data-driven tuning of rational feedforward controllers for general non-commutative MIMO systems. In the developed approach, the nonlinear terms in the non-convex optimisation problem of iterative learning control (ILC) are iteratively circumvented via approximation. This leads to a series of convex optimisation problems that can be solved offline to obtain the parameters of the rational feedforward controller. In addition, by limiting the number of offline iterations an experimentally intensive algorithm is derived, which could be beneficial in the case of severe model mismatch. A simulation study shows that the experimentally efficient approach converges fast through offline iterations and has improved convergence properties through the use of regularisation.
Originele taal-2Engels
TijdschriftInternational Journal of Control
VolumeXX
Nummer van het tijdschriftX
DOI's
StatusE-publicatie vóór gedrukte publicatie - 10 okt. 2024

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