Data-Driven Pole Placement in LMI Regions with Robustness Constraints

This paper proposes a robust learning methodology to place the closed-loop poles in desired convex regions in the complex plane. We considered the system state and input matrices to be unknown and can only use the measurements of the system trajectories. The closed-loop pole placement problem in the linear matrix inequality (LMI) regions is considered a classic robust control problem; however, that requires knowledge about the state and input matrices of the linear system. We bring in ideas from the behavioral system theory and persistency of excitation condition-based fundamental lemma to develop a data-driven counterpart that satisfies multiple closed-loop robustness specifications, such as $\mathcal{D}$-stability and mixed $H2/H{\infty}$ performance specifications. Our formulations lead to data-driven semi-definite programs (SDPs) that are coupled with sufficient theoretical guarantees. We validate the theoretical results with numerical simulations on a third-order dynamic system.