On the improvement of model-predictive controllers
This work addresses a theoretical issue in control systems for researchers, but it is incremental as it builds on existing MPC frameworks without major innovations.
The paper tackles the problem of how the precision of the internal prediction model affects model-predictive controllers, finding that increased precision always improves controller quality, as demonstrated by comparing a DNN-based model to an optimal baseline across three control problems.
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).