Robust sampling time designs for parametric uncertain systems

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Robust experimental design (RED) of sampling time scheduling has been discussed for parametric uncertain systems. Four RED methods, i.e., the pseudo-Bayesian design, the maximin design, the expectation-variance design, and the online experimental redesign, are investigated under the framework of model-based optimal experimental design (OED). Both the D-optimal and the E-optimal criteria are used as performance metrics. Two numerical procedures, the Powell’s method and the semi-definite programming (SDP), are employed to obtain the optimum solution for REDs. The robustness performance of the four REDs are compared using a benchmark enzyme reaction system. In comparison to a typical uniform sampling strategy, the sampling time profiles from REDs are more focused on regions where the dynamic system has higher parametric sensitivities, indicating choice of informative data for parameter identification. The designed sampling strategies are also assessed by bootstrap parameter estimation with randomly generated initial points, where the difference between the REDs can be observed.
Original languageEnglish
Pages (from-to)622-627
Number of pages6
Issue number2
Publication statusPublished - 2020
EventInternational Federation of Automatic Control (IFAC) World Congress 2020: 21st IFAC World Congress - Germany, Berlin, Germany
Duration: 11 Jul 202017 Jul 2020


  • optimal experimental design (OED)
  • robust experimental design (RED)
  • parametric uncertainty
  • sampling time scheduling
  • parameter estimation

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