为糖尿病自动输胰岛素系统设计了人机协同安全控制器。
Operational Safety in Human-in-the-loop Human-in-the-plant Autonomous Systems
- 将人与控制器建模为统一系统，融合人类行为与控制反馈。
- 在真实人体输入下，该控制器是唯一满足安全要求的方案。
- 适用于医疗自动化等高安全需求场景，适合研究人机共融系统者。
控制仿射假设和人类输入作为外部扰动，在实际运行中常因因果性人类行为而被违反。本文提出一种人机协同的人-厂自主系统（HIL-HIP）方法，确保关键安全自主系统的运行安全：将人类与真实世界控制器（RWC）视为统一系统。考虑三类交互：a) 人类与人机接口（HIP）间个性化输入与生物反馈；b) RWC与HIP间通过传感器与执行器的交互；c) 人机接口与真实世界控制器间个性化配置变更与数据反馈。本文扩展控制李雅普诺夫理论，生成在人类行动规划下的障碍函数（CLBF），将人机接口建模为马尔可夫链（自发事件）与模糊推理系统（事件响应）的组合，将真实世界控制器视为黑箱，并将该HIL-HIP模型与神经架构结合，以学习CLBF证书。结果表明，所合成的用于1型糖尿病自动胰岛素输送的HIL-HIP控制器是唯一在人类输入下满足安全要求的控制器。
原文摘要 · Abstract (English)
Control affine assumptions, human inputs are external disturbances, in certified safe controller synthesis approaches are frequently violated in operational deployment under causal human actions. This paper takes a human-in-the-loop human-in-the-plant (HIL-HIP) approach towards ensuring operational safety of safety critical autonomous systems: human and real world controller (RWC) are modeled as a unified system. A three-way interaction is considered: a) through personalized inputs and biological feedback processes between HIP and HIL, b) through sensors and actuators between RWC and HIP, and c) through personalized configuration changes and data feedback between HIL and RWC. We extend control Lyapunov theory by generating barrier function (CLBF) under human action plans, model the HIL as a combination of Markov Chain for spontaneous events and Fuzzy inference system for event responses, the RWC as a black box, and integrate the HIL-HIP model with neural architectures that can learn CLBF certificates. We show that synthesized HIL-HIP controller for automated insulin delivery in Type 1 Diabetes is the only controller to meet safety requirements for human action inputs.
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