Control-Relevant Input Signal Design For Integrating Processes: Application to a Microalgae Raceway Reactor

Sarasij Banerjee, Pablo Otálora, Mohamed El Mistiri, Owais Khan, José Luis Guzmán, Daniel E. Rivera

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

Plants with integrators possess control-relevant modeling requirements that are typically ignored in the literature. Desired closed-loop speeds of response for such systems can differ significantly from their open-loop dynamics, causing conventional system identification guidelines to fail or be inaccurate. One such issue relates to experimental design. This paper presents guidelines for the design of excitation signals for system identification of plants with integrators, with application to the modeling and control of a microalgae raceway reactor. The concept is to excite the system through optimized test signals with control-relevant shaping of their power spectra. This facilitates a shift in emphasis of the identification objective from estimating a model with good open-loop performance to having a model possessing desired closed-loop characteristics. Such a consideration is particularly important when generating informative databases for estimating predictive models for closed-loop control. An illustration for this experimental design procedure is accomplished in this paper through the estimation of ARX-based models and, subsequently, model predictive control of the pH dynamics of an experimental raceway photobioreactor facility hosting sustainable microalgae production.

Original languageEnglish (US)
Pages (from-to)360-365
Number of pages6
JournalIFAC-PapersOnLine
Volume58
Issue number15
DOIs
StatePublished - Jul 1 2024
Event20th IFAC Symposium on System Identification, SYSID 2024 - Boston, United States
Duration: Jul 17 2024Jul 19 2024

Keywords

  • identification for control
  • input and excitation design
  • model predictive control
  • process control
  • robust control

ASJC Scopus subject areas

  • Control and Systems Engineering

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