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：物理人机协作（pHRC）要求在共享工作空间中协调人类动作的同时，兼顾柔顺性与安全性保障。本文提出一种新颖的固定时间自适应神经控制方法，用于处理物理人机协作中出现的时变工作空间约束，并确保预期力交互过程中的柔顺性。该方法融合了柔顺控制、时变积分障碍李雅普诺夫函数（TVIBLF）与固定时间技术的优势，不仅能在与操作者物理接触时实现柔顺性，还能保证时变工作空间约束与快速跟踪误差收敛，且无需对初始条件施加任何限制。此外，设计了一种神经自适应控制律，用于补偿机器人操作臂的未知动力学与扰动，使得所提控制框架整体达到固定时间收敛，并能在无需预先了解机器人动力学与扰动的情况下进行在线学习。最终在仿真二连杆机器人操作臂上验证了所提方法的有效性。仿真结果表明，与现有基于障碍李雅普诺夫函数的控制器相比，所提控制器在跟踪误差与收敛时间方面均表现更优，同时能同步保证柔顺性与安全性。