A Data-driven Approach to Robust Control of Multivariable Systems by Convex Optimization
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 , 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.
Cite
@article{arxiv.1610.08776,
title = {A Data-driven Approach to Robust Control of Multivariable Systems by Convex Optimization},
author = {Alireza Karimi and Christoph Kammer},
journal= {arXiv preprint arXiv:1610.08776},
year = {2017}
}