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Iterative Learning in Repetitive Optimal Control of Linear Dynamic Processes

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Artificial Intelligence and Soft Computing (ICAISC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9692))

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Abstract

Learning of stochastically independent decisions is a well developed theory, the main of its part being pattern recognition algorithms. Learning of dependent decisions for discrete time sequences, e.g., for patterns forming a Markov chain and decision support systems, is also developed, but many classes of problems still remain open. Learning sequences of decisions for systems with continuously running time is still under development. In this paper we provide an approach that is based on the idea of iterative learning for repetitive control systems. A new ingredient is that our system learns to find the optimal control that minimizes a quality criterion and attempts to find it even if there are uncertainties in the system parameters. Such approach requires to record and store full sequences of the system state, which can be done using a camera for monitoring of the system states. The theory is illustrated by an example of a laser cladding process.

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Notes

  1. 1.

    For simplicity of the exposition we omit an easy generalization to the case when also b contains uncertain parameters.

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Acknowledgements

This work has been supported by the National Science Center under grant: 2012/07/B/ST7/01216, internal code 350914 of the Wrocław University of Technology.

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Authors and Affiliations

  1. Faculty of Electronics, Wrocław University of Technology, Wrocław, Poland

    Ewaryst Rafajłowicz & Wojciech Rafajłowicz

Authors
  1. Ewaryst Rafajłowicz
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  2. Wojciech Rafajłowicz
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Corresponding author

Correspondence to Ewaryst Rafajłowicz .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Czestochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Czestochowa, Poland

    Marcin Korytkowski

  3. Częstochowa University of Technology, Czestochowa, Poland

    Rafał Scherer

  4. AGH University of Science and Technology, Krakow, Poland

    Ryszard Tadeusiewicz

  5. University of California, Berkeley, California, USA

    Lotfi A. Zadeh

  6. University of Louisville, Louisville, Kentucky, USA

    Jacek M. Zurada

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Rafajłowicz, E., Rafajłowicz, W. (2016). Iterative Learning in Repetitive Optimal Control of Linear Dynamic Processes. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2016. Lecture Notes in Computer Science(), vol 9692. Springer, Cham. https://doi.org/10.1007/978-3-319-39378-0_60

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  • DOI: https://doi.org/10.1007/978-3-319-39378-0_60

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39377-3

  • Online ISBN: 978-3-319-39378-0

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