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• Two sliding-mode observers are proposed to deal with the uncertainties of the teleoperation system. Finite time estimation can be realized with the proposed observers. • A novel power reaching law is proposed to construct the sliding-mode observer for uncertain kinematics with faster reaching time and larger range of parameter. • Finite time convergence of the tracking errors in teleoperation system and stability of the closed-loop system can be guaranteed with the proposed NTSMC scheme. This study concentrates on the tracking control of teleoperation system subjected to robot uncertainties. The coupling of kinematic and dynamic uncertainties poses a challenge to construct the teleoperation controller. To overcome this difficulty, an observer-based approach is designed to ensure position tracking while compensating for the unfavorable effects arising from the uncertainties. First, two sliding-mode observers together with a novel power reaching law are constructed, upon which, the uncertainties will be estimated in finite time. Next, a controller is proposed to solve the finite-time convergence of the tracking errors. The settling time and the stability of the closed-loop system are derived by Lyapunov's direct method. Simulation results are presented to testify the tracking performance of the suggested control. [ABSTRACT FROM AUTHOR]

In this paper, the problem of neural network control for visual servoing robotic system is addressed, where the unmodeled dynamics and output nonlinearity are taken into account simultaneously. An adaptive neural network module is constructed to approach the unknown dynamics, upon which, the robot dynamics are not required to be linearly decomposable and structurally known. The major superiority of this module lies in its conciseness and the computational-reduction operation. Moreover, the output nonlinearity is considered, and its undesirable effect is subsequently tackled without a prior knowledge of the model parameters in output mechanism. It is proven by the Lyapunov method that the image-space tracking error is driven to an adjustable neighborhood of origin. Numerical simulations and experiments under various situations are used to validate the performance of the proposed adaptive neural network based scheme. [ABSTRACT FROM AUTHOR]