Supervisory observer for parameter and state estimation of nonlinear systems using the DIRECT algorithm

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A supervisory observer is a multiple-model architecture, which estimates the parameters and the states of nonlinear systems. It consists of a bank of state observers, where each observer is designed for some nominal parameter values sampled in a known parameter set. A selection criterion is used to select a single observer at each time instant, which provides its state estimate and parameter value. The sampling of the parameter set plays a crucial role in this approach. Existing works require a sufficiently large number of parameter samples, but no explicit lower bound on this number is provided. The aim of this work is to overcome this limitation by sampling the parameter set automatically using an iterative global optimisation method, called DIviding RECTangles (DIRECT). Using this sampling policy, we start with 1 + 2np parameter samples where np is the dimension of the parameter set. Then, the algorithm iteratively adds samples to improve its estimation accuracy. Convergence guarantees are provided under the same assumptions as in previous works, which include a persistency of excitation condition. The efficacy of the supervisory observer with the DIRECT sampling policy is illustrated on a model of neural populations.


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Externa organisationer
  • University of Lorraine
  • University of Minnesota system
  • University of Melbourne

Ämnesklassifikation (UKÄ) – OBLIGATORISK

  • Reglerteknik
Titel på värdpublikation2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
FörlagInstitute of Electrical and Electronics Engineers Inc.
Antal sidor6
ISBN (elektroniskt)9781509028733
StatusPublished - 2018 jan 18
Peer review utfördJa
Evenemang56th IEEE Annual Conference on Decision and Control, CDC 2017 - Melbourne, Australien
Varaktighet: 2017 dec 122017 dec 15
Konferensnummer: 56


Konferens56th IEEE Annual Conference on Decision and Control, CDC 2017
Förkortad titelCDC 2017