Blanken, L.L.G.

Boeren, F.A.J.

Bruijnen, D.J.H.

Oomen, T.A.E.

English

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 Batch-to-batch rational feedforward tuning : from learning toidentification approachesCitation for published version (APA):Blanken, L. L. G., Boeren, F. A. J., Bruijnen, D. J. H., & Oomen, T. A. E. (2016). Batch-to-batch rationalfeedforward tuning : from learning to identification approaches. In 35th Benelux Meeting on Systems andControl, March 22-24, 2016, Soesterberg, The Netherlands (pp. 122)Document status and date:Published: 01/01/2016Document 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. 2023Batch-to-Batch Rational Feedforward Tuning:From Learning to Identification ApproachesLennart Blanken1, Frank Boeren1, Dennis Bruijnen2, Tom Oomen11Eindhoven University of Technology, Dept. of Mechanical Engineering, Control Systems Technology, The Netherlands2Philips Innovation Services, Mechatronics Technologies, Eindhoven, The Netherlandsl.l.g.blanken@tue.nlIntroductionFeedforward control enables high performance for indus-trial motion systems that perform non-repeating motiontasks. Recently, learning techniques have been proposedthat improve both performance and flexibility to non-repeating tasks in a batch-to-batch fashion by using a ratio-nal parametrization in feedforward control. The aim here isto investigate the merits of these approaches. Experimentalresults on an industrial motion system confirm the theoret-ical findings and illustrate benefits of rational feedforwardtuning in motion systems, including pre- and post-actuation.Batch-to-Batch Feedforward froma System Identification PerspectiveThe goal in batch-to-batch feedforward control is to itera-tively improve control performance by updating a feedfor-ward controller C f f from measured data in a batch-to-batchfashion, see Fig. 1. Here, the batch-to-batch feedforwardtechniques are interpreted in a system identification perspec-tive [4], illustrated in Fig. 2. Essential for the achievableperformance are i) the batches of measured data, ii) the feed-forward controller parameterization, and iii) the optimiza-tion criterion.rj ej yj−Cfb PCff (θj)vjf jFigure 1: In batch-to-batch feedforward control with parameter-ized feedforward, parameters θ of C f f (θ j ) are updated based onmeasured data after each task j.Batch of dataθj+1Parameterization CCriterion VAlgorithmFigure 2: Batch-to-batch feedforward tuning from a system iden-tification perspective. Based on a batch of data, parameterizationC and criterion V , θ j+1 is determined and implemented to obtaina new batch of data.Two techniques to batch-to-batch feedforward are investi-gated: an instrumental variable-based technique [1], and anIterative Learning Control (ILC) based technique [2]. In theframework of Fig. 2, these approaches turn out to be verysimilar. Their algorithms to compute θ j+1 can be interpretedin terms of a standard ILC update law, given byθ j+1 = Qθ j + Le j , (1)with corresponding robustness and learning matrices Q, L .Implementation & Experimental ResultsInterestingly, rational feedforward controllers can also beused to generate pre-actuation by means of stable inversionprocedures, see, e.g., [5]. The proposed approaches are im-plemented on a wafer stage. The results in Fig. 3 demon-strate preactuation, used to prevent transient errors at thestart of the motion task, and postactuation, used to reduceresidual vibrations in the system. This feature is key for thepotential performance improvement of rational feedforward.0.047 0.049 0.05104080120160Time [s]f5[N](a) Pre-actuation.0.125 0.127 0.129-60-3003060Time [s](b) Post-actuation.Figure 3: The rational parameterization ( ) enables pre-and post-actuation of the system, in contrast to the polynomialparametrization ( ). The start and end times of the motion taskare indicated by black dashed lines.References[1] F. Boeren, T. Oomen and M. Steinbuch, Iterative motion feedfor-ward tuning: A data-driven approach based on instrumental variable iden-tification, Control Engineering Practice, vol. 37, 11-19, 2015.[2] J. Bolder and T. Oomen, Rational basis functions in iterative learn-ing control - With experimental verification on a motion system, IEEETransactions on Control Systems Technology, 23(22):722-729, 2015.[3] D. Bristow, M. Tharayil, and A. Alleyne, A Survey of IterativeLearning Control, IEEE Control Systems, 26(3):96-114, 2006.[4] L. Ljung, System identification - Theory for the User, Upper SaddleRiver, New York, USA: Prentice Hall, 1999.[5] Q. Zou and S. Devasia, Preview-based stable-inversion for outputtracking of linear systems, ASME Journal of Dynamic Systems, Measure-ment and Control, 121(1):625-630, 1999.Book of Abstracts 35th Benelux Meeting on Systems and Control122

Batch-to-batch rational feedforward tuning : from learning to identification approaches

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 Batch-to-batch rational feedforward tuning : from learning toidentification approachesCitation for published version (APA):Blanken, L. L. G., Boeren, F. A. J., Bruijnen, D. J. H., & Oomen, T. A. E. (2016). Batch-to-batch rationalfeedforward tuning : from learning to identification approaches. In 35th Benelux Meeting on Systems andControl, March 22-24, 2016, Soesterberg, The Netherlands (pp. 122)Document status and date:Published: 01/01/2016Document 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: 05. Oct. 2023Batch-to-Batch Rational Feedforward Tuning:From Learning to Identification ApproachesLennart Blanken1, Frank Boeren1, Dennis Bruijnen2, Tom Oomen11Eindhoven University of Technology, Dept. of Mechanical Engineering, Control Systems Technology, The Netherlands2Philips Innovation Services, Mechatronics Technologies, Eindhoven, The Netherlandsl.l.g.blanken@tue.nlIntroductionFeedforward control enables high performance for indus-trial motion systems that perform non-repeating motiontasks. Recently, learning techniques have been proposedthat improve both performance and flexibility to non-repeating tasks in a batch-to-batch fashion by using a ratio-nal parametrization in feedforward control. The aim here isto investigate the merits of these approaches. Experimentalresults on an industrial motion system confirm the theoret-ical findings and illustrate benefits of rational feedforwardtuning in motion systems, including pre- and post-actuation.Batch-to-Batch Feedforward froma System Identification PerspectiveThe goal in batch-to-batch feedforward control is to itera-tively improve control performance by updating a feedfor-ward controller C f f from measured data in a batch-to-batchfashion, see Fig. 1. Here, the batch-to-batch feedforwardtechniques are interpreted in a system identification perspec-tive [4], illustrated in Fig. 2. Essential for the achievableperformance are i) the batches of measured data, ii) the feed-forward controller parameterization, and iii) the optimiza-tion criterion.rj ej yj−Cfb PCff (θj)vjf jFigure 1: In batch-to-batch feedforward control with parameter-ized feedforward, parameters θ of C f f (θ j ) are updated based onmeasured data after each task j.Batch of dataθj+1Parameterization CCriterion VAlgorithmFigure 2: Batch-to-batch feedforward tuning from a system iden-tification perspective. Based on a batch of data, parameterizationC and criterion V , θ j+1 is determined and implemented to obtaina new batch of data.Two techniques to batch-to-batch feedforward are investi-gated: an instrumental variable-based technique [1], and anIterative Learning Control (ILC) based technique [2]. In theframework of Fig. 2, these approaches turn out to be verysimilar. Their algorithms to compute θ j+1 can be interpretedin terms of a standard ILC update law, given byθ j+1 = Qθ j + Le j , (1)with corresponding robustness and learning matrices Q, L .Implementation & Experimental ResultsInterestingly, rational feedforward controllers can also beused to generate pre-actuation by means of stable inversionprocedures, see, e.g., [5]. The proposed approaches are im-plemented on a wafer stage. The results in Fig. 3 demon-strate preactuation, used to prevent transient errors at thestart of the motion task, and postactuation, used to reduceresidual vibrations in the system. This feature is key for thepotential performance improvement of rational feedforward.0.047 0.049 0.05104080120160Time [s]f5[N](a) Pre-actuation.0.125 0.127 0.129-60-3003060Time [s](b) Post-actuation.Figure 3: The rational parameterization ( ) enables pre-and post-actuation of the system, in contrast to the polynomialparametrization ( ). The start and end times of the motion taskare indicated by black dashed lines.References[1] F. Boeren, T. Oomen and M. Steinbuch, Iterative motion feedfor-ward tuning: A data-driven approach based on instrumental variable iden-tification, Control Engineering Practice, vol. 37, 11-19, 2015.[2] J. Bolder and T. Oomen, Rational basis functions in iterative learn-ing control - With experimental verification on a motion system, IEEETransactions on Control Systems Technology, 23(22):722-729, 2015.[3] D. Bristow, M. Tharayil, and A. Alleyne, A Survey of IterativeLearning Control, IEEE Control Systems, 26(3):96-114, 2006.[4] L. Ljung, System identification - Theory for the User, Upper SaddleRiver, New York, USA: Prentice Hall, 1999.[5] Q. Zou and S. Devasia, Preview-based stable-inversion for outputtracking of linear systems, ASME Journal of Dynamic Systems, Measure-ment and Control, 121(1):625-630, 1999.Book of Abstracts 35th Benelux Meeting on Systems and Control122

Batch-to-batch rational feedforward tuning : from learning to identification approaches

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