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10 Robust Reduced Order Filtering

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Part of the Lecture Notes in Control and Information Science book series (LNCIS,volume 343)

Abstract

In this chapter we shall address topics dealing with adding robustness to the design of reduced order filters. We will show how one can treat a broad range of noise or disturbance terms characterized as having bounded energy, rather than as having white noise characteristics. For such additive arbitrary disturbances, we show how to guarantee a bound on the ratio of the energy in the error to the energy in the disturbances. In [1], a rigorous derivation of the results contained here was developed. We will present here, a derivation based on game theory which more closely corresponds to the calculus of variations approach taken throughout this text, and follows the method suggested by Banavar and Speyer [2].

Keywords

  • Kalman Filter
  • Riccati Equation
  • Reduce Order Model
  • Full Order
  • Matrix Riccati Equation

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Jalali, A.A., Sims†, C.S., Famouri, P. 10 Robust Reduced Order Filtering. In: Reduced Order Systems. Lecture Notes in Control and Information Science, vol 343. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11597018_10

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  • DOI: https://doi.org/10.1007/11597018_10

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34358-5

  • Online ISBN: 978-3-540-34359-2

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