Application of Deep Reinforcement Learning to Event-Triggered Control for Networked Artificial Pancreas Systems
For patients with type 1 diabetes using artificial pancreas systems, this work addresses the need for energy-efficient operation in networked control by reducing communication updates without sacrificing glycemic control.
This paper proposes a deep reinforcement learning-based event-triggered controller for networked artificial pancreas systems that reduces communication frequency while maintaining control performance, achieving improved communication efficiency.
This paper proposes a deep reinforcement learning (DRL)-based event-triggered controller design for networked artificial pancreas (AP) systems. Although existing DRL-based AP controllers typically assume periodic control updates, networked control systems (NCSs) require a reduction in communication frequency to achieve energy-efficient operation, which is directly tied to control updates. However, jointly learning both insulin dosing and update timing significantly increases the complexity of the learning problem. To alleviate this complexity, we develop a practical DRL-based controller design that avoids explicitly learning update timing by introducing a rule-based criterion defined by changes in blood glucose. As a result, decision-making occurs at irregular intervals, and the problem is naturally formulated as a semi-Markov decision process (SMDP), for which we extend a standard DRL algorithm. Numerical experiments demonstrate that the proposed method improves communication efficiency while maintaining control performance.