Decentralized Neural Backstepping Control Applied to a Robot Manipulator

作者：Garcia Hernandez Ramon^*; Ruz Hernandez Jose A; Rullan Lara Jose L

来源：International Journal of Advanced Robotic Systems, 2013, 10(1): 78.

DOI：10.5772/54015

• Decentralized control
• High-Order Neural Networks
• Extended Kalman Filter
• Backstepping

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.

• 出版日期2013-1-30