Self-aware Power Management for Maintaining Event Detection Probability of Supercapacitor-powered Cyber-physical Systems

Abstract

In this article, the self-aware power management framework is investigated for maintaining event detection probability of supercapacitor-powered cyber-physical systems, with a radar network system as an example. Maintaining the event detection probability of the radar network is decomposed as a problem of controlling the quality of service of each network node. Then a power management method based on model predictive control and particle swarm optimization is proposed for tracking the reference quality of service of each node while satisfying the operation constraints. The effectiveness of the proposed method is demonstrated through three simulation studies that cover both single node and network scenarios. In addition, to support the proposed power management method, an online state of charge prediction method is developed for the supercapacitor. The online prediction method adopts a supercapacitor model that describes both the ohmic leakage and charge redistribution phenomena and uses online model updating to more accurately capture the supercapacitor behavior and estimate the stored energy.