Application of Deep Reinforcement Learning to Event-Triggered Control for Networked Artificial Pancreas Systems

TL;DR

This paper introduces a DRL-based event-triggered control method for networked artificial pancreas systems, reducing communication frequency while maintaining effective blood glucose regulation.

Contribution

The paper develops a practical DRL controller that avoids explicit learning of update timing, formulating the problem as an SMDP for improved efficiency.

Findings

Improved communication efficiency in control updates.

Maintained blood glucose regulation performance.

Extended DRL algorithms for semi-Markov decision processes.

Abstract

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.