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On identification methods for direct data‐driven controller tuning
Author(s) -
van Heusden Klaske,
Karimi Alireza,
Söderström Torsten
Publication year - 2011
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.1213
Subject(s) - identification (biology) , controller (irrigation) , control theory (sociology) , noise (video) , computer science , consistency (knowledge bases) , set (abstract data type) , control engineering , control (management) , engineering , artificial intelligence , botany , agronomy , image (mathematics) , biology , programming language
In non‐iterative data‐driven controller tuning, a set of measured input/output data of the plant is used directly to identify the optimal controller that minimizes some control criterion. This approach allows the design of fixed‐order controllers, but leads to an identification problem where the input is affected by noise, and not the output as in standard identification problems. Several solutions that deal with the effect of measurement noise in this specific identification problem have been proposed in the literature. The consistency and statistical efficiency of these methods are discussed in this paper and the performance of the different methods is compared. The conclusions offer a guideline on how to solve the data‐driven controller tuning problem efficiently. Copyright © 2010 John Wiley & Sons, Ltd.