Abstract
This paper presents a discrete-time decentralized control strategy for trajectory tracking of a seven degrees of freedom (DOF) redundant robot. A high order neural network (HONN) is used to approximate a decentralized control law designed by the backstepping technique as applied to a block strict feedback form (BSFF). The neural network learning is performed online using Kalman filtering. The motion of each joint is controlled independently using only local angular position and velocity measurements. The proposed controller is validated via simulations.
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