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Model reduction for classes of uncertain, multi-dimensional, parameter varying and non-linear systems

  • Part C Modeling, Identification And Estimation
  • Conference paper
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Learning, control and hybrid systems

Part of the book series: Lecture Notes in Control and Information Sciences ((LNCIS,volume 241))

Abstract

This paper surveys recently proposed approaches for the model reduction of certain classes of uncertain, multi-dimensional, parameter varying and non-linear systems. It is shown that each of these systems may be written using a similar formulation. Balanced truncation model reduction, based on the solution of two Linear Matrix Inequalities (LMI’s) are discussed for each class of system and the similarities (and differences) highlighted.

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Author information

Authors and Affiliations

  1. Department of Engineering, University of Cambridge, CB2 1PZ, Cambridge, UK

    Y. -C. Chu

Authors
  1. K. Glover
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  2. P. J. Goddard
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  3. Y. -C. Chu
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Editor information

Yutaka Yamamoto PhD Shinji Hara PhD

Additional information

This paper is dedicated with warm friendship to Bruce Francis and Mathukumalli Vidyasagar on the occasion of their 50th birthdays.

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© 1999 Springer-Verlag London Limited

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Glover, K., Goddard, P.J., Chu, Y.C. (1999). Model reduction for classes of uncertain, multi-dimensional, parameter varying and non-linear systems. In: Yamamoto, Y., Hara, S. (eds) Learning, control and hybrid systems. Lecture Notes in Control and Information Sciences, vol 241. Springer, London. https://doi.org/10.1007/BFb0109734

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  • DOI: https://doi.org/10.1007/BFb0109734

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-85233-076-7

  • Online ISBN: 978-1-84628-533-2

  • eBook Packages: Springer Book Archive

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