Empirical Study of Dynamic Regret in Online Model Predictive Control for Linear Time-Varying Systems

TL;DR

This paper empirically evaluates the performance of online Model Predictive Control in Linear Time-Varying systems, focusing on dynamic regret, prediction errors, and horizons to bridge theoretical guarantees with practical applications.

Contribution

It provides the first empirical validation of theoretical dynamic regret bounds for MPC in LTV systems, analyzing the impact of prediction errors and horizons.

Findings

Dynamic regret increases with prediction errors.

Longer prediction horizons improve control performance.

Empirical results support theoretical performance bounds.

Abstract

Model Predictive Control (MPC) is a widely used technique for managing timevarying systems, supported by extensive theoretical analysis. While theoretical studies employing dynamic regret frameworks have established robust performance guarantees, their empirical validation remains sparse. This paper investigates the practical applicability of MPC by empirically evaluating the assumptions and theoretical results proposed by Lin et al. [2022]. Specifically, we analyze the performance of online MPC under varying prediction errors and prediction horizons in Linear Time-Varying (LTV) systems. Our study examines the relationship between dynamic regret, prediction errors, and prediction horizons, providing insights into the trade-offs involved. By bridging theory and practice, this work advances the understanding and application of MPC in real-world scenarios.