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arXiv · 1809.10121

Safely Learning to Control the Constrained Linear Quadratic Regulator

Sarah Dean·Stephen Tu·Nikolai Matni·Benjamin Recht
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Abstract

We study the constrained linear quadratic regulator with unknown dynamics, addressing the tension between safety and exploration in data-driven control techniques. We present a framework which allows for system identification through persistent excitation, while maintaining safety by guaranteeing the satisfaction of state and input constraints. This framework involves a novel method for synthesizing robust constraint-satisfying feedback controllers, leveraging newly developed tools from system level synthesis. We connect statistical results with cost sub-optimality bounds to give non-asymptotic guarantees on both estimation and controller performance.

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Sarah Dean, Stephen Tu, Nikolai Matni, Benjamin Recht. 2018-09-26. Safely Learning to Control the Constrained Linear Quadratic Regulator. https://arxiv.org/abs/1809.10121

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