Precision on Demand: Propositional Logic for Event-Trigger Threshold Regulation
<|reference_start|>Precision on Demand: Propositional Logic for Event-Trigger Threshold Regulation: We introduce a novel event-trigger threshold (ETT) regulation mechanism based on the quantitative semantics of propositional logic (PL). We exploit the expressiveness of the PL vocabulary to deliver a precise and flexible specification of ETT regulation based on system requirements and properties. Additionally, we present a modified ETT regulation mechanism that provides formal guarantees for satisfaction/violation detection of arbitrary PL properties. To validate our proposed method, we consider a convoy of vehicles in an adaptive cruise control scenario. In this scenario, the PL operators are used to encode safety properties and the ETTs are regulated accordingly, e.g., if our safety metric is high there can be a higher ETT threshold, while a smaller threshold is used when the system is approaching unsafe conditions. Under ideal ETT regulation conditions in this safety scenario, we show that reductions between 41.8 - 96.3% in the number of triggered events is possible compared to using a constant ETT while maintaining similar safety conditions.<|reference_end|>
@article{tang2024precision,
    title={Precision on Demand: Propositional Logic for Event-Trigger Threshold
  Regulation},
    author={Valdemar Tang, Claudio Gomes and Daniel Lucani},
    journal={arXiv preprint arXiv:2408.14473},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.14473},
    primaryClass={eess.SY cs.LO cs.SY}
}