Non-conservative Design of Robust Tracking Controllers Based on Input-output Data
Abstract
This paper studies worst-case robust optimal tracking using noisy input-output data. We utilize behavioral system theory to represent system trajectories, while avoiding explicit system identification. We assume that the recent output data used in the data-dependent representation are noisy and we provide a non-conservative design procedure for robust control based on optimization with a linear cost and LMI constraints. Our methods rely on the parameterization of noise sequences compatible with the data-dependent system representation and on a suitable reformulation of the performance specification, which further enable the application of the S-lemma to derive an LMI optimization problem. The performance of the new controller is discussed through simulations.
Cite
@article{arxiv.2101.00488,
title = {Non-conservative Design of Robust Tracking Controllers Based on Input-output Data},
author = {Liang Xu and Mustafa Sahin Turan and Baiwei Guo and Giancarlo Ferrari-Trecate},
journal= {arXiv preprint arXiv:2101.00488},
year = {2021}
}