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An automatic tuner with short experiment and probabilistic plant parameterization
Author(s) -
Soltesz Kristian,
Mercader Pedro,
Baños Alfonso
Publication year - 2016
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.3640
Subject(s) - robustness (evolution) , control theory (sociology) , probabilistic logic , parametric statistics , transfer function , sensitivity (control systems) , covariance , computer science , noise (video) , tuner , system identification , algorithm , mathematics , engineering , statistics , control (management) , artificial intelligence , data mining , measure (data warehouse) , telecommunications , biochemistry , chemistry , radio frequency , electronic engineering , electrical engineering , image (mathematics) , gene
Summary 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 theℋ ∞norms of the sensitivity function, while minimizing load disturbance integral error 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. Copyright © 2016 John Wiley & Sons, Ltd.