Neural network-based adaptive position tracking control for bilateral teleoperation under constant time delay

The trajectory tracking problem for the teleoperation systems is addressed in this paper. Two neural network-based controllers are designed for the teleoperation system in free motion. First, with the defined synchronization variables containing the velocity error and the position error between master and slaver, a new adaptive controller using the acceleration signal is designed to guarantee the position and velocity tracking performance between the master and the slave manipulators. Second, with the acceleration signal unavailable, a controller with the new synchronization variables is proposed such that the trajectory tracking error between the master and the slave robots asymptotically converges to zero. By choosing proper Lyapunov functions, the asymptotic tracking performance with these two controllers is proved without the knowledge of the upper bound of the neural network approximation error and the external disturbance. Finally, the simulations are performed to show the effectiveness of the proposed methods.