An automatic tuner with short experiment and probabilistic plant parameterization

Kristian Soltesz, Pedro Mercader, Alfonso Baños

Research output: Contribution to journalArticlepeer-review

6 Citations (SciVal)
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Abstract

A novel automatic tuning strategy is proposed. It is based on an experiment of very short duration, followed by simultaneous identification of LTI model parameters and an estimate of their error covariance. The parametric uncertainty model is subsequently exploited to design linear controllers with magnitude bounds on some closed-loop transfer function of interest, such as the sensitivity function. The method is demonstrated through industrially relevant examples. Robustness is enforced through probabilistic constraints on the H∞ norms of the sensitivity function, while minimizing load disturbance integral error (IE) to ensure performance. To demonstrate the strength of the proposed method, identification for the mentioned examples is carried out under a high level of measurement noise.
Original languageEnglish
Pages (from-to)1857-1873
JournalInternational Journal of Robust and Nonlinear Control
Volume27
Issue number11
DOIs
Publication statusPublished - 2017 Jul

Subject classification (UKÄ)

  • Control Engineering

Keywords

  • Automatic tuning
  • robust identification
  • parametric uncertainty
  • uncertainty propagation

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