Adaptive neural learning fixed-time control for uncertain teleoperation system with time-delay and time-varying output constraints

Restricted by operation time and workspace, the end effectors of robots need to complete the teleoperation tasks within the limited time while adhering to the physical constraints. Meanwhile, time-delay has an extremely detrimental influence on stability and transparency. In order to meet the constraints of convergence time, control performance and workspace, an adaptive neural learning fixed-time control scheme incorporating an integral barrier Lyapunov function is proposed for the first time. Neural networks are utilized to reconstruct environmental forces and approximate the total uncertainty introduced by robots and the environment. Instead of directly transmitting high-frequency force signals, the neural network is used to fit the environmental force before transmitting the virtual environment parameters to the leader, which effectively avoids the passive issue and improves the transparency of the teleoperation system. The results show that the error signals converge into the neighborhood of the zero domain in fixed-time and the output is directly constrained within the prescribed time-varying boundary. In comparison with other existing research, the control performance of the teleoperation system has been improved to a certain extent with the proposed control method. Simulations and experiments are conducted to verify the feasibility and availability of the proposed control strategy with the teleoperation platform composed of two Phantom Omni haptic devices.