  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.
