A Self-Triggered Approach for Co-Design of MPC and Computing Resource Allocation

In this article, we consider the tradeoff problem of control performance and computing resource utilization in edge controller in the context of smart manufacturing. In the Internet factory, an edge device needs to complete multiple computing tasks including control tasks under the condition of limited computing resources. Therefore, we propose a time division multiplexing-based self-triggered model predictive control algorithm and a computing resource allocation algorithm based on reinforcement learning to solve the optimal control input and the optimal computing resource allocation scheme at the same time. Through the information interaction between the controller and the computing resource management unit, the proposed algorithm can calculate the future optimal control strategy and the resource allocation scheme according to the real-time state of physical system and computing resource utilization requirement. Through simulation analysis, we get the tradeoff relationship between control performance and computing resource allocation, and verify the effect of the method in the real-time operation of the system.