With the increasing use of advanced control methods such as model predictive control for nonlinear systems, the demand for real-time computational power continues to increase. Meeting this demand can be especially challenging when multiple nonlinear systems need to be controlled with the potentially limited embedded hardware of the individual systems. Utilizing a central computing unit, such as a cloud, can provide the required additional computing power at the expense of increased economic and energy costs.In this work, we consider a scenario where embedded controllers based on neural networks that imitate a model predictive controller need to be supported by a central computing unit in case of errors or model changes. To minimize the necessary amount of central computing used to guarantee a safe simultaneous operation of multiple systems, we propose a reinforcement learning-based scheduling of the different necessary computing tasks. Furthermore, we show that the proposed control structure is safe for the closed-loop control of nonlinear systems. A case study illustrates the benefits of the proposed control structure.

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.