Non-conservative Design of Robust Tracking Controllers Based on Input-output Data
Proceedings of the 3rd Conference on Learning for Dynamics and Control, PMLR 144:138-149, 2021.
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.