Real-time Decentralized Neural Block Control: Application to a Two Dof Robot Manipulator
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چکیده
This paper presents a discrete-time decentralized control scheme for trajectory tracking of a two degrees of freedom (DOF) robot manipulator. A modified recurrent high order neural network (RHONN) structure is used to identify the plant model and based on this model, a discrete-time control law is derived, which combines block control and sliding mode techniques. The neural network learning is performed on-line by extended Kalman filtering (EKF). The controllers are designed for each joint using only local angular position and velocity measurements, simplifying computation complexity. The proposed scheme is implemented in real-time to control a two DOF robot manipulator.
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تاریخ انتشار 2010