Adaptive Neural network Control for Time-delay Teleoperation with Uncertainties

Most studies on teleoperation systems only consider dynamical uncertainties. However, many practical applications contain unknown external disturbances, mechanical parameter and dynamics uncertainties. In this paper, a class of time-delay teleoperation system with internal mechanical friction and external disturbance uncertainties is investigated. An adaptive neural network control approach is proposed, in which adaptive laws are designed to estimate unknown internal friction and external disturbances, and radial basis function neural networks are used to approximate the unknown parameters in the system model. The proposed control scheme can guarantee that the tracking errors eventually converge to a small neighborhood around zero. Simulation results are used to illustrate the performance of the suggested approach.