Computing the average inter-sample time of event-triggered control using quantitative automata. (February 2023) - de Albuquerque Gleizer, Gabriel; Mazo, Manuel
Abstract: Event-triggered control (ETC) is a major recent development in cyber–physical systems due to its capability of reducing resource utilization in networked devices. However, while most of the ETC literature reports simulations indicating massive reductions in the sampling required for control, no method so far has been capable of quantifying these results. In this work, we propose an approach through finite-state abstractions to do formal quantification of the traffic generated by ETC of linear systems, in particular aiming at computing its smallest average inter-sample time (SAIST). The method involves abstracting the traffic model through l -complete abstractions, finding the cycle of minimum average length in the graph associated to it, and verifying whether this cycle is an infinitely recurring traffic pattern. The method is proven to be robust to sufficiently small model uncertainties, which allows its application to compute the SAIST of ETC of nonlinear systems. Highlights: It is challenging to predict the sampling performance of event-triggered control (ETC) We devise an algorithm to compute the average inter-sample time of ETC systems The algorithm uses finite abstractions and Karp's algorithm to estimate such metric The estimate is exact when Karp's minimum cycle is a cyclic behavior of the system The algorithm is robust, and error bounds can be obtained when terminated early