文章基本信息

标题：Decentralized Neural Backstepping Control Applied to a Robot Manipulator
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作者：Ramon Garcia-Hernandez ; Jose A. Ruz-Hernandez ; Jose L. Rullan-Lara 等
期刊名称：International Journal of Advanced Robotic Systems
印刷版ISSN：1729-8806
电子版ISSN：1729-8814
出版年度：2013
卷号：10
DOI：10.5772/54015
语种：English
出版社：SAGE Publications
摘要：This paper presents a discrete-time decentralized control scheme for trajectory tracking of a two degrees of freedom (DOF) robot manipulator. 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 weights for each neural network are adapted online by an extended Kalman filter training algorithm. The motion for each joint is controlled independently using only local angular position and velocity measurements. The stability analysis for the closed-loop system via the Lyapunov approach is included. Finally, the real-time results show the feasibility of the proposed control scheme using a robot manipulator.
关键词：Decentralized control; High-Order Neural Networks; Extended Kalman Filter; Backstepping