Blanken, L.L.G.

Boeren, F.A.J.

Bruijnen, D.J.H.

Oomen, T.A.E.

Steinbuch, M.

English

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 Advances in optimization-based feedforward tuningCitation for published version (APA):Boeren, F. A. J., Bruijnen, D. J. H., Blanken, L. L. G., Oomen, T. A. E., & Steinbuch, M. (2014). Advances inoptimization-based feedforward tuning. In 2nd DSPE Conference on Precision Mechatronics (pp. 137-138)Document status and date:Published: 01/01/2014Please 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: 08. Feb. 2024Advances in Optimization-Based Feedforward Tuning Frank	  Boeren1,	  Dennis	  Bruijnen2,	  Lennart	  Blanken1,	  Maarten	  Steinbuch1,	  Tom	  Oomen1	  1	  Control	  Systems	  Technology,	  Dept.	  of	  Mechanical	  Engineering,	  Eindhoven	  University	  of	  Technology	  2	  Philips	  Innovation	  Services	  f.a.j.boeren@tue.nl	  Feedforward control is the key method to improve the servo performance of high-precision motion systems. In typical applications of motion control, a low-order feedforward controller is designed that approximates the inverse of the system. For example, the servo performance of systems with dominant rigid-body dynamics can be effectively enhanced by acceleration feedforward following the second law of Newton.   In view of stringent requirements on throughput and accuracy expected for next-generation motion systems, advances in feedforward control are needed. An increase in throughput results in more aggressive setpoints which directly influence positioning accuracy. The contribution of this research is to present two new aspects in feedforward control. First, more advanced feedforward controllers are proposed to enable an improved approximation of the inverse of the system. Second, an automated tuning approach is presented which enables accurate and fast tuning of more advanced feedforward controllers. Regarding the first contribution, aggressive setpoints for next-generation motion system excite, in addition to rigid-body dynamics (intended), also the flexible dynamics of the system (not intended). Since acceleration feedforward is only effective at low frequencies, servo performance is limited by the low feedforward order. Hence, increasing the order of the feedforward controller improves servo performance since the inverse of a system containing performance-limiting flexible dynamics is approximated better. In this research, more advanced feedforward is proposed by establishing connections to input shaping [1] and rational feedforward design in ILC [2]. Regarding the second contribution, one of the key difficulties in feedforward control is tuning the parameters of a feedforward controller. On the one hand, manual tuning by means of trial-and-error is a widely successful approach in industrial practice. On the other hand, optimization-based methods, e.g., machine-in-the-loop optimization for feedforward control as depicted in Fig. 1, has significant advantages over manual tuning. In machine-in-the-loop optimization, the parameters of a feedforward controller are automatically tuned based on measured data from a previous task. In this research, a new approach is proposed to determine the feedforward controller by means of a machine-in-the-loop optimization [3]. The new theory relies on fundamental aspects in instrumental variable-based system identification [4]. The advantages are that the approach: i) has an increased accuracy in the presence of noise and disturbances and ii) closely resembles manual tuning.  Experimental results confirm the value of the new contributions to feedforward control, as verified on the NForcer setup depicted in Fig. 2.  	  Figure	  1:	  Two	  degree-­‐of-­‐freedom	  control	  configuration	  for	  machine-­‐in-­‐the-­‐loop	  optimization 	  Figure	  2:	  Nforcer	  experimental	  setup	  References [1] D. Bruijnen, N. van Dijk and J. Songtao, A practical comparison of feedforward control design techniques for high-precision motion systems, Proc. of the DSPE Conference on Precision Mechatronics, Deurne, The Netherlands, pp. 53-54, 2012.  [2] J. Bolder, T. Oomen and M. Steinbuch, Exploiting rational basis functions in iterative learning control, Proc. of the Conference on Decision and Control, Florence, Italy, pp. 7321-7326, 2013.  [3]   F. Boeren and T. Oomen, Iterative feedforward control: a closed-loop identification problem and a solution, Proc. of the Conference on Decision and Control, Florence, Italy, pp. 6694-6699, 2013.  [4] T. Söderström and P. Stoica, Instrumental Variable Methods for System Identification, Springer-Verlag, Berlin, Germany, vol. 57 of Lecture Notes in Control and Information Sciences, 1983.  

Advances in optimization-based feedforward tuning

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Advances in optimization-based feedforward tuning

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