Fixed-time adaptive neural control for physical human-robot collaboration with time-varying workspace constraints

Effective physical human–robot collaboration (pHRC) requires strict safety guarantees since robots coordinate with human actions in a shared workspace. Moreover, compliance is essential, enabling robots to adjust their stiffness and behavior in response to human interaction forces. This paper presents a novel fixed-time adaptive neural control method for handling safety constraints that occur in physical human–robot collaboration while also guaranteeing compliance during intended force interactions. The proposed method 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 and then extended to the simulated UR10 robot. Simulation results show that the proposed controller is superior in the sense of both the tracking error and convergence time compared with the existing barrier Lyapunov function-based controllers while simultaneously guaranteeing compliance and safety.