Neural Network Control of Robot Manipulators and Nonlinear Systems
Neural Network Control of Robot Manipulators and Nonlinear Systems
Brief paper: Synchronization of bilateral teleoperators with time delay
Automatica (Journal of IFAC)
Brief paper: An adaptive controller for nonlinear teleoperators
Automatica (Journal of IFAC)
A combined backstepping and small-gain approach to robust adaptive fuzzy output feedback control
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Adaptive backstepping fuzzy control for nonlinearly parameterized systems with periodic disturbances
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Delay-dependent stability criteria of teleoperation systems with asymmetric time-varying delays
IEEE Transactions on Robotics
Automatica (Journal of IFAC)
Bilateral teleoperation: An historical survey
Automatica (Journal of IFAC)
Passive Bilateral Teleoperation With Constant Time Delay
IEEE Transactions on Robotics
On tracking performance in bilateral teleoperation
IEEE Transactions on Robotics
A Globally Stable PD Controller for Bilateral Teleoperators
IEEE Transactions on Robotics
Multilayer neural-net robot controller with guaranteed tracking performance
IEEE Transactions on Neural Networks
IEEE Transactions on Fuzzy Systems
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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.