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Conference Papers Year : 2016

Continuous-time system identification using binary measurements

Mathieu Pouliquen
Abdelhak Goudjil
Olivier Gehan
  • Function : Author
  • PersonId : 941736
Eric Pigeon

Abstract

This paper investigates the identification of Continuous-Time models based on binary observations. Currently, no identification algorithm has been proposed in this field. The reason is twofold: first the time-domain differentiation operator of a such model structure prevents the use of existing identification methods based on binary observations, second the simple knowledge of binary observations on the output prevents the use of existing Continuous-Time identification methods. In this paper a two steps identification algorithm is derived. It is based on the combined use of a specific input signal and Support Vector Machines for the reconstruction of a high resolution output signal. This high resolution output signal is then used for the estimation of a Continuous-Time model. An extension for the identification of a MISO system is also proposed. Some simulation results are given in order to illustrate the validity of the proposed method.
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Dates and versions

hal-01825010 , version 1 (27-06-2018)

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Mathieu Pouliquen, Abdelhak Goudjil, Olivier Gehan, Eric Pigeon. Continuous-time system identification using binary measurements. 2016 IEEE 55th Conference on Decision and Control (CDC), Dec 2016, Las Vegas, United States. pp.3787-3792, ⟨10.1109/CDC.2016.7798840⟩. ⟨hal-01825010⟩
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