Data-Driven LQR Control Design

TL;DR

This paper introduces a data-driven method for designing optimal LQR controllers directly from input-state data, bypassing the need for explicit system models, and demonstrates its effectiveness through simulations and power supply experiments.

Contribution

It proposes a novel data-driven approach for LQR control design that directly computes the gain from data, advancing control design without explicit system identification.

Findings

The method converges to the optimal LQR gain as data increases.

Simulation results confirm the effectiveness of the approach.

Experimental validation on power supply systems demonstrates practical applicability.

Abstract

This paper presents a data-driven solution to the discrete-time infinite horizon LQR problem. The state feedback gain is computed directly from a batch of input and state data collected from the plant. Simulation examples illustrate the convergence of the proposed solution to the optimal LQR gain as the number of Markov parameters tends to infinity. Experiments in an uninterruptible power supply are presented, which demonstrate the practical applicability of the design methodology.