Data-Driven Self-Triggered Control for Linear Networked Control Systems

This paper considers data-driven control of unknown linear discrete-time systems under a self-triggered transmission scheme. While self-triggered control has received much attention in the literature, its design and implementation typically require explicit model knowledge. Due to the difficulties in obtaining accurate models and the abundance of data in applications, this paper proposes a novel data-driven self-triggered control scheme for unknown systems. To this end, we begin by presenting a model-based self-triggered scheme (STS) in form of quadratic matrix inequalities, on the basis of an equivalent switched system representation. Combining the model-based triggering law and a data-based system representation, a data-driven STS is developed leveraging pre-collected input-state data for predicting the next transmission instant while ensuring system stability. A data-based method for co-designing the controller gain and the triggering matrix is then provided. Finally, a numerical simulation showcases the efficacy of STS in reducing transmissions as well as practicality of the proposed co-design methods.