Data-Driven LQR Control Design

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.

arXiv commentpreprint submitted to L-CSS

Cited by in corpus (8)

• Data-based stabilization of unknown bilinear systems with guaranteed basin of attraction
• Data-Driven Optimal Control of Bilinear Systems
• Data-based guarantees of set invariance properties
• On a probabilistic approach to synthesize control policies from example datasets
• On the crowdsourcing of behaviors for autonomous agents
• Virtual reference feedback tuning with robustness constraints: A swarm intelligence solution
• Data-Assisted Control -- A Framework Development by Exploiting NASA GTM Platform
• Data-Driven Minimum-Energy Controls for Linear Systems