A data-driven approach to robust control of multivariable systems by convex optimization. (November 2017)

• Record Type: Journal Article
• Authors: Karimi, Alireza; Kammer, Christoph
• Is Part Of: Automatica. Volume 85(2017)
• Page Start: 227
• Page End: 233
• Publication Date: 2017-11
• Subjects: Data-driven control -- Robust control -- Convex optimization; Automatic control -- Periodicals; Automation -- Periodicals; 629.805
• DOI: 10.1016/j.automatica.2017.07.063
• Languages: English
• ISSNs: 0005-1098

Abstract

Abstract: The frequency-domain data of a multivariable system in different operating points is used to design a robust controller with respect to the measurement noise and multimodel uncertainty. The controller is fully parameterized in terms of matrix polynomial functions and can be formulated as a centralized, decentralized or distributed controller. All standard performance specifications like H 2, H ∞ and loop shaping are considered in a unified framework for continuous- and discrete-time systems. The control problem is formulated as a convex–concave optimization problem and then convexified by linearization of the concave part around an initial controller. The performance criterion converges monotonically to a local optimum or a saddle point in an iterative algorithm. The effectiveness of the method is compared with fixed-structure controller design methods based on non-smooth optimization via multiple simulation examples.