On the improvement of model-predictive controllers

TL;DR

This paper demonstrates that increasing the precision of the internal prediction model in model-predictive control systems consistently enhances overall controller performance, as shown through comparisons with an optimal baseline model across various control problems.

Contribution

It provides empirical evidence that improving the internal prediction model in MPC directly leads to better control outcomes, highlighting the importance of model accuracy.

Findings

Higher PM accuracy improves controller performance

DNN-based PM approaches optimal accuracy in tested scenarios

Model improvements benefit overall control without modifying action selection

Abstract

This article investigates synthetic model-predictive control (MPC) problems to demonstrate that an increased precision of the internal prediction model (PM) automatially entails an improvement of the controller as a whole. In contrast to reinforcement learning (RL), MPC uses the PM to predict subsequent states of the controlled system (CS), instead of directly recommending suitable actions. To assess how the precision of the PM translates into the quality of the model-predictive controller, we compare a DNN-based PM to the optimal baseline PM for three well-known control problems of varying complexity. The baseline PM achieves perfect accuracy by accessing the simulation of the CS itself. Based on the obtained results, we argue that an improvement of the PM will always improve the controller as a whole, without considering the impact of other components such as action selection (which, in this article, relies on evolutionary optimization).