Approximate Model Predictive Control Based on Neural Networks in a Cloud-Based Environment

Efficient approximations of predictive controllers using neural networks can enable the deployment of highperformance controllers virtually everywhere. Such approximations can run on simple embedded hardware but have significant drawbacks. The approximate controllers need a computationally expensive training in advance, and they do not inherit the feasibility and stability guarantees of the original predictive controllers. This paper considers possible future control architectures in an industrial setting where a cloud, or a centralized computing unit, can be used to simultaneously mitigate these drawbacks by supervising the performance of the embedded controllers based on the computation of safe sets and predicted constraint violations, which can trigger a direct control by the cloud as well as a retraining of the embedded approximate controllers. We demonstrate the potential of the proposed approach with a simulation study.