Experiment design for batch-to-batch model-based learning control

Marco Forgione, Xavier Bombois, Paul M.J. van den Hof

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)

Abstract

An Experiment Design framework for dynamical systems which execute multiple batches is presented in this paper. After each batch, a model of the system dynamics is refined using the measured data. This model is used to synthesize the controller that will be applied in the next batch. Excitation signals may be injected into the system during each batch. From one hand, perturbing the system worsens the control performance during the current batch. On the other hand, the more informative data set will lead to a better identified model for the following batches. The role of Experiment Design is to choose the proper excitation signals in order to optimize a certain performance criterion defined on the set of batches that is scheduled. A total cost is defined in terms of the excitation and the application cost altogether. The excitation signals are designed by minimizing the total cost in a worst case sense. The Experiment Design is formulated as a Convex Optimization problem which can be solved efficiently using standard algorithms. The applicability of the method is demonstrated in a simulation study.

Original languageEnglish
Title of host publication2013 American Control Conference, ACC 2013
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages3912-3917
Number of pages6
ISBN (Electronic)978-1-4799-0178-4
ISBN (Print)978-1-4799-0178-4
DOIs
Publication statusPublished - 11 Sep 2013
Event2013 1st American Control Conference, ACC 2013 - Washington, DC, United States
Duration: 17 Jun 201319 Jun 2013

Conference

Conference2013 1st American Control Conference, ACC 2013
CountryUnited States
CityWashington, DC
Period17/06/1319/06/13

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  • Cite this

    Forgione, M., Bombois, X., & van den Hof, P. M. J. (2013). Experiment design for batch-to-batch model-based learning control. In 2013 American Control Conference, ACC 2013 (pp. 3912-3917). [6580437] Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ACC.2013.6580437