A Direct Data-Driven Intermittent Control and Learning via Lyapunov-Guided Attraction Region Estimation With Neural Feedback Loop Design

This paper investigates neural networks-based state-dependent intermittent control (SDIC) from a data-driven perspective, considering unknown continuous-time linear systems subject to external disturbances. Instead of relying on precise system models, the proposed approach utilizes offline-collected data to develop a data-driven SDIC scheme. An ℓ1-norm-based convex optimization method is employed to estimate the domain of attraction (DOA), enabling the partitioning of the state space into certified control regions. This facilitates state-dependent control updates that reduce communication burden while preserving system stability. Compared with existing methods, the proposed scheme offers greater flexibility, as its triggering mechanism does not depend on prior model knowledge or a small estimated DOA. Furthermore, it is practically implementable: a neural feedback controller is constructed to satisfy Lyapunov-based stability conditions using only a data-driven linear matrix inequality (LMI), without requiring complex additional assumptions. The effectiveness of the proposed strategy is demonstrated through simulations on a real HVAC system. The learned DOA-based region partitioning allows the controller to adapt to varying environmental conditions while ensuring stability and energy efficiency. Comprehensive simulation results validate the practicality and performance of the proposed control framework.