Data-Driven Optimal Control of Affine Systems: A Linear Programming Perspective

Abstract

In this letter, we discuss the problem of optimal control for affine systems in the context of data-driven linear programming. First, we introduce a unified framework for the fixed point characterization of the value function, Q-function and relaxed Bellman operators. Then, in a model-free setting, we show how to synthesize and estimate Bellman inequalities from a small but sufficiently rich dataset. To guarantee exploration richness, we complete the extension of Willem's fundamental lemma to affine systems.

References in corpus (1)

• Linear tracking MPC for nonlinear systems Part II: The data-driven case

Cited by in corpus (4)

• Linear tracking MPC for nonlinear systems Part II: The data-driven case
• An overview of systems-theoretic guarantees in data-driven model predictive control
• On a Stochastic Fundamental Lemma and Its Use for Data-Driven Optimal Control
• Bounded Linear Programs for Data-Driven Optimal Control via Moment-Matching