Adaptive Terminal Sliding Mode Control for Time-delay Teleoperation with Uncertainties

For teleoperation systems, it is desirable that the tracking errors can converge in a finite time because it means the task will be accomplished better and faster. This paper propose an adaptive terminal sliding mode bilateral controller with guaranteed continuous finite time for a class of time-delay teleoperation system with internal mechanical friction and external disturbance, in which the controller is designed based on the terminal sliding mode method, the radial basis function neural network is used to estimate uncertainties in the teleoperation system and the neural network weights are updated by the adaptive laws. By using Lyapunov stability theory, the stability of the control system is analyzed, and it is proved that the tracking errors will converge to zero in finite time. Compared with a previous adaptive neural network controller, simulation results illustrate that the proposed controller has a shorter error convergence time.