A data-driven approach to robust control of multivariable systems by convex optimization

• Autores: Alireza Karimi, Cristoph Kammel
• Localización: Automatica: A journal of IFAC the International Federation of Automatic Control, ISSN 0005-1098, Vol. 85, 2017, págs. 227-233
• Idioma: inglés
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• Resumen

  • 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.