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Identification of Linear Continuous-time Systems Based on Iterative Learning Control

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Recent Advances in Learning and Control

Part of the book series: Lecture Notes in Control and Information Sciences ((LNCIS,volume 371))

Abstract

One of the most important issues in control system design is to obtain an accurate model of the plant to be controlled. Though most of the existing identification methods are described in discrete-time, it would be more appropriate to have continuous-time models directly from the sampled I/O data. This paper presents a novel approach for such direct identification of continuous-time systems based on iterations. The method achieves identification through iterative learning control concepts in the presence of heavy measurement noise. The robustness against measurement noise is achieved through (i) projection of continuous-time I/O signals onto a finite dimensional parameter space, and (ii) noise tolerant learning laws. The method can be easily applied to system identification in closed loop. The effectiveness of the method is demonstrated through numerical examples for systems including non-minimum phase one.

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Author information

Authors and Affiliations

  1. Department of Systems Science, Kyoto University, Japan

    Toshiharu Sugie

Authors
  1. Toshiharu Sugie
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Editor information

Editors and Affiliations

  1. Département d’Ingénierie Mathématique, Université catholique de Louvain, avenue Georges Lemaître, 4, B-1348, Louvain-la-Neuve, Belgium

    Vincent D. Blondel PhD

  2. Information Systems Laboratory Department of Electrical Engineering, Stanford University, Stanford, CA, 94305-9510, USA

    Stephen P. Boyd PhD

  3. Bio-Mimetic Control Research Center, RIKEN (The Institute of Physical and Chemical Research), Anagahora, Shimoshidami, Moriyama-ku, Nagoya, 463-0003, Japan

    Hidenori Kimura PhD

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© 2008 Springer-Verlag Berlin Heidelberg

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Sugie, T. (2008). Identification of Linear Continuous-time Systems Based on Iterative Learning Control. In: Blondel, V.D., Boyd, S.P., Kimura, H. (eds) Recent Advances in Learning and Control. Lecture Notes in Control and Information Sciences, vol 371. Springer, London. https://doi.org/10.1007/978-1-84800-155-8_15

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  • DOI: https://doi.org/10.1007/978-1-84800-155-8_15

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84800-154-1

  • Online ISBN: 978-1-84800-155-8

  • eBook Packages: EngineeringEngineering (R0)

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