运行时间保证的神经网络控制系统的系统过程分析
Systems Theoretic Process Analysis of a Run Time Assured Neural Network Control System
摘要：
本研究考虑了使用神经网络控制系统（NNCS）的深度强化学习（RL）战术自动驾驶仪的安全约束识别和运行时间保证（RTA）问题。该研究研究了NNCS执行自主编队飞行的特定用例，同时RTA系统提供碰撞避免和地理围栏保证。首先，应用系统理论事故模型和过程（STAMP）来识别事故、危险和安全约束，并定义地面站、有人驾驶飞行引导和代理无人驾驶僚机的功能控制系统框图。然后，系统理论过程分析（STPA）应用于地面站，有人驾驶飞行的领导，代理无人驾驶僚机，和僚机的内部元素的相互作用，以确定不安全的控制动作，导致每个场景，和安全要求，以减轻风险。本研究首次将STAMP和STPA应用于由RTA约束的NNCS。
英文摘要：
This research considers the problem of identifying safety constraints and developing Run Time Assurance (RTA) for Deep Reinforcement Learning (RL) Tactical Autopilots that use neural network control systems (NNCS). This research studies a specific use case of an NNCS performing autonomous formation flight while an RTA system provides collision avoidance and geofence assurances. First, Systems Theoretic Accident Models and Processes (STAMP) is applied to identify accidents, hazards, and safety constraints as well as define a functional control system block diagram of the ground station, manned flight lead, and surrogate unmanned wingman. Then, Systems Theoretic Process Analysis (STPA) is applied to the interactions of the the ground station, manned flight lead, surrogate unmanned wingman, and internal elements of the wingman aircraft to identify unsafe control actions, scenarios leading to each, and safety requirements to mitigate risks. This research is the first application of STAMP and STPA to an NNCS bounded by RTA.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
