Data-Driven Sub-Optimal LQ Regulator for Linear Input-Delay Systems based on Informativity
It addresses the problem of optimal control for input-delay systems when a model is unavailable, offering a data-driven solution with guaranteed performance.
This paper proposes a data-driven method to synthesize a sub-optimal LQ regulator for linear input-delay systems using noisy input-state data, achieving a prescribed performance level via convex optimization. Numerical simulations validate the approach.
This paper proposes a novel informativity-based data-driven synthesis method for a sub-optimal linear quadratic (LQ) regulator for linear input-delay systems from noisy input-state data. Exploiting the augmented state structure of input-delay systems with a known delay length, we derive a linear matrix inequality (LMI) condition for the data-driven synthesis of the augmented state-feedback controller that achieves a prescribed LQ performance level for every plant model consistent with the data. The proposed LMI condition enables efficient controller synthesis via convex optimization. Numerical simulations demonstrate the effectiveness of the proposed method. The trade-off between the achievable LQ performance and the uncertainty in the data is also clarified through a numerical example.