Decentralized Neural Backstepping Control Applied to a Robot Manipulator | Zendy

Ramón García-Hernández | Zendy; José A. Ruz-Hernández | Zendy; Jose L. Rullan-Lara | Zendy

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Decentralized Neural Backstepping Control Applied to a Robot Manipulator

Author(s) -

Ramón García-Hernández,

José A. Ruz-Hernández,

Jose L. Rullan-Lara

Publication year - 2013

Publication title -

international journal of advanced robotic systems

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.394

H-Index - 46

eISSN - 1729-8814

pISSN - 1729-8806

DOI - 10.5772/54015

Subject(s) - backstepping , computer science , control theory (sociology) , artificial neural network , lyapunov function , trajectory , lyapunov stability , block (permutation group theory) , robot , position (finance) , control (management) , artificial intelligence , adaptive control , nonlinear system , mathematics , physics , geometry , finance , quantum mechanics , astronomy , economics

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

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