Decentralized Neural Backstepping Control for an Industrial PA10-7CE Robot Arm

This paper presents a discrete-time decentralized control strategy for trajectory tracking of a seven degrees of freedom (DOF) robot arm. 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 by extended Kalman filter. The local controller for each joint use only local angular position and velocity measurements. The feasibility of the proposed scheme is illustrated via simulation.

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