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Robust control of systems with sector nonlinearities via convex optimization: A data‐driven approach
Author(s) -
Nicoletti Achille,
Karimi Alireza
Publication year - 2018
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.4439
Subject(s) - bounded function , control theory (sociology) , convex optimization , nonlinear system , mathematical optimization , stability (learning theory) , regular polygon , robust control , closed loop , optimization problem , computer science , mathematics , control (management) , engineering , control engineering , mathematical analysis , physics , geometry , quantum mechanics , artificial intelligence , machine learning
Summary In this paper, a new data‐driven method for designing robust controllers is proposed for systems with sector‐bounded nonlinearities and multimodel uncertainties. The results from the circle criterion are used to generate necessary and sufficient convex constraints that guarantee the stability of the closed‐loop system. The main feature of the proposed approach is that only the frequency response data of the linear part of the system is used for guaranteeing the stability of the closed‐loop nonlinear system. Additionally, a convex optimization problem is formulated to ensure H ∞ performance with respect to the fundamental component of a sector‐bounded nonlinearity. The case study illustrates how the proposed method can be used to control uncertain systems that are subject to sector‐bounded nonlinearities.