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).

翻译：本文研究了综合模型预测控制（MPC）问题，证明内部预测模型（PM）精度的提升自动带来控制器整体性能的改善。与强化学习（RL）不同，MPC利用预测模型来预测被控系统（CS）的后续状态，而非直接推荐合适动作。为评估预测模型精度如何转化为模型预测控制器的质量，我们针对三个不同复杂度的经典控制问题，将基于深度神经网络（DNN）的预测模型与最优基线预测模型进行了比较。该基线预测模型通过访问被控系统本身的仿真实现完美精度。基于实验结果，我们认为在不考虑动作选择等其他组件影响的情况下（本文中动作选择依赖于进化优化），预测模型的改进必将提升控制器整体性能。