Identification of continuous-time systems utilising Kautz basis functions from sampled-data

María Coronel, Rodrigo Carvajal, Juan C. Agüero

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations


In this paper we address the problem of identifying a continuous-time deterministic system utilising sampled-data with instantaneous sampling. We develop an identification algorithm based on Maximum Likelihood. The exact discrete-time model is obtained for two cases: i) known continuous-time model structure and ii) using Kautz basis functions to approximate the continuous-time transfer function. The contribution of this paper is threefold: i) we show that, in general, the discretisation of continuous-time deterministic systems leads to several local optima in the likelihood function, phenomenon termed as aliasing, ii) we discretise Kautz basis functions and obtain a recursive algorithm for constructing their equivalent discrete-time transfer functions, and iii) we show that the utilisation of Kautz basis functions to approximate the true continuous-time deterministic system results in convex log-likelihood functions. We illustrate the benefits of our proposal via numerical examples.

Original languageEnglish
Pages (from-to)536-541
Number of pages6
Issue number2
StatePublished - 2020
Externally publishedYes
Event21st IFAC World Congress 2020 - Berlin, Germany
Duration: 12 Jul 202017 Jul 2020


  • Continuous-time model
  • Discrete-time model
  • Kautz basis functions
  • Maximum Likelihood
  • System identification


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