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

arXiv: https://arxiv.org/abs/1610.08776 · HTML: https://mlsanity.com/paper/1610.08776/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2016.

## Built on by (most cited first, 1 of 1)

- [Clustering Techniques for Stable Linear Dynamical Systems with applications to Hard Disk Drives](https://mlsanity.com/paper/2311.10322/) (2023)

## Abstract (arXiv)

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 H2, 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 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.