Hierarchical Event-Triggered Systems: Safe Learning of Quasi-Optimal Deadline Policies
<|reference_start|>Hierarchical Event-Triggered Systems: Safe Learning of Quasi-Optimal Deadline Policies: We present a hierarchical architecture to improve the efficiency of event-triggered control (ETC) in reducing resource consumption. This paper considers event-triggered systems generally as an impulsive control system in which the objective is to minimize the number of impulses. Our architecture recognizes that traditional ETC is a greedy strategy towards optimizing average inter-event times and introduces the idea of a deadline policy for the optimization of long-term discounted inter-event times. A lower layer is designed employing event-triggered control to guarantee the satisfaction of control objectives, while a higher layer implements a deadline policy designed with reinforcement learning to improve the discounted inter-event time. We apply this scheme to the control of an orbiting spacecraft, showing superior performance in terms of actuation frequency reduction with respect to a standard (one-layer) ETC while maintaining safety guarantees.<|reference_end|>
@article{ong2024hierarchical,
    title={Hierarchical Event-Triggered Systems: Safe Learning of Quasi-Optimal
  Deadline Policies},
    author={Pio Ong and Manuel Mazo Jr. and Aaron D. Ames},
    journal={arXiv preprint arXiv:2409.09812},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.09812},
    primaryClass={eess.SY cs.SY math.OC}
}