On the improvement of model-predictive controllers
L. Féret A. Gepperth S. Lambeck
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).