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- W2105755853 abstract "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." @default.
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- W2105755853 date "2013-01-01" @default.
- W2105755853 modified "2023-09-22" @default.
- W2105755853 title "Decentralized Neural Backstepping Control Applied to a Robot Manipulator" @default.
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- W2105755853 doi "https://doi.org/10.5772/54015" @default.
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