Avoiding Semi-Infinite Programming in Distributionally Robust Control Based on Mean-Variance Metrics

TL;DR

This paper introduces a novel approach to distributionally robust control that avoids semi-infinite programming by reformulating the problem as a mean-variance optimization, simplifying solution procedures and improving performance.

Contribution

It proposes a method that eliminates semi-infinite programming in distributionally robust control by using a penalty-based reformulation into mean-variance problems, enabling easier solution via Riccati equations.

Findings

The proposed method reduces the original problem to a mean-variance optimization.

Numerical experiments show lower maximum discounted costs compared to conventional methods.

The control laws in linear-quadratic settings are derived from Riccati equations.

Abstract

Conventional stochastic control methods have several limitations. They focus on optimizing the average performance and, in some cases, performance variability; however, their problem settings still require an explicit specification of the probability distributions that determine the system's stochastic behavior. Distributionally robust control (DRC) methods have recently been developed to address these challenges. However, many DRC approaches involve handling infinitely many inequalities. For instance, DRC problems based on the Wasserstein distance are commonly obtained by solving semi-infinite programming (SIP) problems. Our proposed method eliminates the need for SIP when solving discrete-time, discounted, distributionally robust optimal control problems. By introducing a penalty term based on a specific distributional distance, we establish upper bounds, and under appropriate conditions, demonstrate the equivalence between distributionally robust optimization problems and mean-variance minimization problems. This reformulation reduces the original DRC problem to a discounted mean-variance cost optimization problem. In linear-quadratic regulator settings, the corresponding control laws are obtained by solving the Riccati equation. Numerical experiments demonstrate that the theoretical maximum value of the discounted cumulative cost for the proposed method is lower than that for the conventional method.