Robust Resource-Aware Self-triggered Model Predictive Control

• DOI: 10.48550/arxiv.2112.00860
• Publication Date: 2022-01-01
• Authors: Yingzhao Lian; Yuning Jiang; Naomi Stricker; Lothar Thiele; Colin N. Jones
• Keywords: 0209 industrial biotechnology; robust optimal control; batteries; constrained linear-systems; Systems and Control (eess.SY); 02 engineering and technology; dynamic scheduling; internet of things; Electrical Engineering and Systems Science - Systems and Control; feedback control; ellipsoids; mpc; FOS: Electrical engineering, electronic engineering, information engineering; self-triggered model predictive control; uncertainty; predictive control

Accepted to L-CSS and ACC 2022<br/>The wide adoption of wireless devices in the Internet of Things requires controllers that are able to operate with limited resources, such as battery life. Operating these devices robustly in an uncertain environment, while managing available resources, increases the difficultly of controller design. This paper proposes a robust self-triggered model predictive control approach to optimize a control objective while managing resource consumption. In particular, a novel zero-order-hold aperiodic discrete-time feedback control law is developed to ensure robust constraint satisfaction for continuous-time linear systems.<br/>