For parameter identifications of robot systems, most existing works have focused on the
estimation veracity, but few works of literature are concerned with the convergence speed. In …

As an imitation of the biological nervous systems, neural networks (NNs), which have been
characterized as powerful learning tools, are employed in a wide range of applications, such …

Due to strongly coupled nonlinearities of the grasped dual-arm robot and the internal forces
generated by grasped objects, the dual-arm robot control with uncertain kinematics and …

Robot manipulators are playing increasingly significant roles in scientific researches and
engineering applications in recent years. Using manipulators to save labors and increase …

In this paper, robust and adaptive nonsingular fast terminal sliding-mode (NFTSM) control
schemes for the trajectory tracking problem are proposed with known or unknown upper …

In this paper, a neural networks (NNs) enhanced telerobot control system is designed and
tested on a Baxter robot. Guaranteed performance of the telerobot control system is …

This paper investigates a difficult problem of tracking control for robotic manipulations with
guaranteed high accuracy. Uncertain kinematics, unknown torques including unknown …

In this paper, a force sensorless control scheme based on neural networks (NNs) is
developed for interaction between robot manipulators and human arms in physical collision …

In this paper, a learning-based robust tracking control scheme is proposed for a quadrotor
unmanned aerial vehicle system. The quadrotor dynamics are modeled including time …

This paper addresses a long-standing yet well documented open problem on task-space
trajectory tracking control of robotic manipulators subject to both uncertain dynamics and …