Safe and efficient multi-system neural controllers via reinforcement learning-based scheduling

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.