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arXiv · 1610.08776

A Data-driven Approach to Robust Control of Multivariable Systems by Convex Optimization

Alireza Karimi·Christoph Kammer
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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 parametrized in terms of matrix polynomial functions and can be formulated as a centralized, decentralized or distributed controller. All standard performance specifications like $H_2$, $H_\infty$ 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 optimal solution in an iterative algorithm. The effectiveness of the method is compared with fixed-structure controllers using non-smooth optimization and with full-order optimal controllers via simulation examples. Finally, the experimental data of a gyroscope is used to design a data-driven controller that is successfully applied on the real system.

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Alireza Karimi, Christoph Kammer. 2017-08-09. A Data-driven Approach to Robust Control of Multivariable Systems by Convex Optimization. https://doi.org/10.1016/j.automatica.2017.07.063

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