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Identification, uncertainty characterization and robust control synthesis applied to large flexible structures control
Author(s) -
Bayard David S.,
Chiang Richard Y.
Publication year - 1998
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/(sici)1099-1239(199802)8:2<97::aid-rnc321>3.0.co;2-1
Subject(s) - robust control , key (lock) , identification (biology) , control engineering , usable , computer science , matlab , controller (irrigation) , system identification , implementation , control system , set (abstract data type) , software , raw data , robustness (evolution) , control (management) , control theory (sociology) , engineering , data mining , artificial intelligence , software engineering , computer security , world wide web , biology , operating system , agronomy , programming language , measure (data warehouse) , botany , electrical engineering , chemistry , biochemistry , gene
This paper demonstrates an approach to frequency domain identification for the explicit purpose of designing robust H ∞ controllers. The approach transforms raw experimental data into a plant set estimate directly usable by modern robust control design software (e.g., Matlab Robust Control Toolboxes). A key issue in control design from raw data is the question of whether the controller will work when applied to the true system. The main feature of this approach is that the resulting controller is guaranteed to work as designed (when applied to the true system) to a prescribed statistical confidence. While the overall methodology addresses key theoretical issues, it has at the same time been specifically designed to support practical implementations. A simulation example is included to demonstrate the overall approach. © 1998 John Wiley & Sons, Ltd.