Abstract The glycemia regulation is a significant challenge in the Artificial Pancreas (AP) scenario. Several control systems have been developed in the last years, many of them requiring meal announcements. Therefore, if the patients skip the meal announcement or make a mistake in the estimation of the amount of carbohydrates, the control performance will be negatively affected. In this extended version of our previous work, we present a Model Predictive Controller (MPC) for the AP in which the meal is treated as a disturbance to be estimated by an Unknown Input Observer (UIO). The MPC constraints are expressed in terms of Signal Temporal Logic (STL) specifications. Indeed, in the AP some requirements result in hard constraints (in particular, absolutely avoid hypoglycemia and absolutely avoid severe hyperglycemia) and some other in soft constraints (avoid a prolonged hyperglycemia) and STL is suitable for expressing such requirements. The achieved results are obtained using the BluSTL toolbox, which allows to synthesize model predictive controllers with STL constraints. We report simulations showing that the proposed approach, avoiding unnecessary restrictions, provides safe trajectories in correspondence of higher unknown disturbance.

中文翻译：

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基于信号时间逻辑规范的葡萄糖浓度模型预测控制与未知膳食发生

摘要 在人工胰腺 (AP) 方案中，血糖调节是一项重大挑战。过去几年开发了多种控制系统，其中许多都需要进餐通知。因此，如果患者跳过进餐公告或错误估计碳水化合物的量，则会对控制性能产生负面影响。在我们之前工作的这个扩展版本中，我们为 AP 提供了一个模型预测控制器 (MPC)，其中膳食被视为由未知输入观察者 (UIO) 估计的干扰。MPC 约束以信号时间逻辑 (STL) 规范表示。实际上，在 AP 中，某些要求会导致硬约束（尤其是 绝对避免低血糖和绝对避免严重的高血糖）和其他一些软约束（避免长时间的高血糖），STL 适合表达这样的要求。所取得的结果是使用 BluSTL 工具箱获得的，该工具箱允许综合具有 STL 约束的模型预测控制器。我们报告的模拟表明，所提出的方法避免了不必要的限制，提供了与更高未知干扰相对应的安全轨迹。