Objective: The artificial pancreas (AP) has shown promising potential in achieving closed-loop glucose control for individuals with type 1 diabetes mellitus (T1DM). However, designing an effective control policy for the AP remains challenging due to the complex physiological processes, delayed insulin response, and inaccurate glucose measurements. While model predictive control (MPC) offers safety and stability through the dynamic model and safety constraints, it lacks individualization and is adversely affected by unannounced meals. Conversely, deep reinforcement learning (DRL) provides personalized and adaptive strategies but faces challenges with distribution shifts and substantial data requirements. Methods: We propose a hybrid control policy for the artificial pancreas (HyCPAP) to address the above challenges. HyCPAP combines an MPC policy with an ensemble DRL policy, leveraging the strengths of both policies while compensating for their respective limitations. To facilitate faster deployment of AP systems in real-world settings, we further incorporate meta-learning techniques into HyCPAP, leveraging previous experience and patient-shared knowledge to enable fast adaptation to new patients with limited available data. Results: We conduct extensive experiments using the FDA-accepted UVA/Padova T1DM simulator across three scenarios. Our approaches achieve the highest percentage of time spent in the desired euglycemic range and the lowest occurrences of hypoglycemia. Conclusion: The results clearly demonstrate the superiority of our methods for closed-loop glucose management in individuals with T1DM. Significance: The study presents novel control policies for AP systems, affirming the great potential of proposed methods for efficient closed-loop glucose control.

翻译：摘要：目标：人工胰腺（AP）在实现1型糖尿病（T1DM）患者闭环血糖控制方面展现出巨大潜力。然而，由于复杂生理过程、胰岛素响应延迟以及葡萄糖测量不准确等问题，设计有效的AP控制策略仍具有挑战性。模型预测控制（MPC）虽能通过动态模型和安全约束提供安全性与稳定性，但其缺乏个体化能力且易受未预报进餐影响。反之，深度强化学习（DRL）可提供个性化自适应策略，但面临分布偏移及数据需求过大的问题。方法：本文提出一种面向人工胰腺的混合控制策略（HyCPAP），通过融合MPC策略与集成DRL策略，既发挥两者优势又弥补各自局限性。为加速AP系统在真实场景中的部署，我们进一步将元学习技术融入HyCPAP，利用历史经验与患者共享知识，在有限数据条件下实现对新患者的快速适配。结果：基于FDA认证的UVA/Padova T1DM模拟器，我们在三种场景下开展大量实验。所提方法在目标血糖范围内占比最高，且低血糖事件发生率最低。结论：实验结果明确证明了本方法在T1DM患者闭环血糖管理中的优越性。意义：本研究为AP系统提出了新型控制策略，证实了所提方法在实现高效闭环血糖控制方面的巨大潜力。