Oomen, Tom

Poot, M.M.

Portegies, Jim

English

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 Feedforward Tuning with Input NonlinearitiesCitation for published version (APA):Poot, M. M., Portegies, J., & Oomen, T. (2023). Feedforward Tuning with Input Nonlinearities: Application toMagnetic Saturation in Wirebonders. Abstract from 42nd Benelux Meeting on Systems and Control 2023,Elspeet, Netherlands.Document license:CC BY-NC-NDDocument status and date:Published: 01/01/2023Document Version:Publisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers)Please check the document version of this publication:• A submitted manuscript is the version of the article upon submission and before peer-review. There can beimportant differences between the submitted version and the official published version of record. Peopleinterested in the research are advised to contact the author for the final version of the publication, or visit theDOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and pagenumbers.Link to publicationGeneral rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.            • Users may download and print one copy of any publication from the public portal for the purpose of private study or research.            • You may not further distribute the material or use it for any profit-making activity or commercial gain            • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, pleasefollow below link for the End User Agreement:www.tue.nl/taverneTake down policyIf you believe that this document breaches copyright please contact us at:openaccess@tue.nlproviding details and we will investigate your claim.Download date: 04. Oct. 2023Feedforward Tuning with Input Nonlinearities: Application toMagnetic Saturation in Wirebonders1Maurice Poot2,⇤, Jim Portegies3, Tom Oomen2,42Control Systems Technology Group, Dept. of Mechanical Engineering, Eindhoven University of Technology, The Netherlands3CASA, Dept. of Mathematics and Computer Science, Eindhoven University of Technology, The Netherlands4Delft Center for Systems and Control, Delft University of Technology, The Netherlands⇤Email: m.m.poot@tue.nl1 BackgroundThe increasing demands on throughput and accuracy ofsemiconductor manufacturing equipment necessitates accu-rate feedforward motion control that includes compensationof input nonlinearities. In [1], iterative learning control withpolynomial basis functions (ILCBF) is introduced to enableextrapolation of the motion tasks. To achieve high track-ing accuracy and task flexibility for nonlinear systems, ex-tensions are necessary that compensate input nonlinearities,e.g., magnetic saturation in linear actuators [2].2 Problem formulationThe aim of this research is to develop a data-driven feedfor-ward tuning approach consisting of a Wiener feedforward,i.e., linear parameterization F(q) with an output nonlinear-ity h(·,f), see Fig. 1, for Hammerstein systems.3 ApproachThe developed approach exploits norm-optimal iterativelearning control (NOILC) to learn a feedforward signal fromdata that minimizes the error and utilizes a control-relevantcost function to learn q ,f of the Wiener feedforward [3].4 ResultsExperimental results, see Fig. 2, on a wirebonder subjectto magnetic saturation show a reduction in tracking error us-ing the developed approach compared to the linear approach.Moreover, for motion tasks with varying maximum acceler-ations, the mass parameter is significantly more consistentin the developed approach, indicating task flexibility.5 Conclusion and outlookThe developed Wiener feedforward approach achieves hightracking accuracy and task flexibility for Hammerstein sys-tems. Ongoing research focuses on online learning in an1This work is supported by ASM Pacific Technology. The authors thankJilles van Hulst, Kelvin Kai Wa Yan, and Dragan Kostić for their contribu-tions to this research.C rfevyg PxuF (✓) h(·, )PfullFigure 1: Proposed control scheme with Wiener feedforward f =h(F(q)r,f).Figure 2: Top: error signals of linear ( ), the developed ( ),and NOILC ( ) approach. Bottom: mass per setpointacceleration of linear ( ) and the developed ( )approach.ILC setting, extensions to non-parametric models for h, andanalysis of position dependency of the magnetic saturation.References[1] J. van de Wijdeven and O. Bosgra, “Using basis func-tions in iterative learning control: analysis and design the-ory,” Int. J. Control, vol. 83, no. 4, pp. 661–675, apr 2010.[2] H. Polinder, “Modelling a linear PM motor includingmagnetic saturation,” in Int. Conf. Power Electron. Mach.Drives, vol. 2002, no. 487. IEE, 2002, pp. 632–637.[3] J. van Hulst, M. Poot, D. Kostić, K. W. Yan, J. Porte-gies, and T. Oomen, “Feedforward Control in the Presenceof Input Nonlinearities: A Learning-based Approach,” inModel. Estim. Control Conf., Jersey City, New Jersey, 2022.Book of Abstracts 42nd Benelux Meeting on Systems and Control122

Feedforward Tuning with Input Nonlinearities:Application to Magnetic Saturation in Wirebonders

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Feedforward Tuning with Input Nonlinearities:Application to Magnetic Saturation in Wirebonders

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