Physical human-robot collaboration (pHRC) requires both compliance and safety guarantees since robots coordinate with human actions in a shared workspace. This paper presents a novel fixed-time adaptive neural control methodology for handling time-varying workspace constraints that occur in physical human-robot collaboration while also guaranteeing compliance during intended force interactions. The proposed methodology combines the benefits of compliance control, time-varying integral barrier Lyapunov function (TVIBLF) and fixed-time techniques, which not only achieve compliance during physical contact with human operators but also guarantee time-varying workspace constraints and fast tracking error convergence without any restriction on the initial conditions. Furthermore, a neural adaptive control law is designed to compensate for the unknown dynamics and disturbances of the robot manipulator such that the proposed control framework is overall fixed-time converged and capable of online learning without any prior knowledge of robot dynamics and disturbances. The proposed approach is finally validated on a simulated two-link robot manipulator. Simulation results show that the proposed controller is superior in the sense of both tracking error and convergence time compared with the existing barrier Lyapunov functions based controllers, while simultaneously guaranteeing compliance and safety.
翻译:本文介绍了一种新型的固定时间适应性神经控制方法,用于处理在人体-机器人协作中发生的时间变化工作空间限制,同时保证在预期的武力互动期间遵守规定。拟议方法结合了合规控制、时间变化式整体屏障Lyapunov功能(TVIBLF)和固定时间技术的好处,这些好处不仅在与人类操作者进行物理接触时就实现了合规,而且还保证了时间变化式工作空间限制和快速跟踪误差趋同,对初始条件没有任何限制。此外,设计神经适应性控制法是为了补偿机器人操纵者未知的动态和干扰,使拟议的控制框架在总体上固定时间趋同,能够在不事先了解机器人动态和干扰的情况下在线学习。拟议方法最终在模拟的双连机器人操纵器上得到验证。模拟结果显示,拟议的控制者在跟踪错误和趋同时间与基于现有Lyapunov的屏障控制器功能的意义上都优越,同时保证合规和安全。</s>