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Adaptive Control for Robotic Manipulators Base on RBF Neural Network

Author(s): MA Jing | Zhang Wenhui | Zhu Haiping

ISSN 1693-6930

Volume: 11;
Issue: 3;
Date: 2013;
Original page

Keywords: Neural network | Adaptive control | Robotic Manipulators | Global asymptotic stability

An adaptive neural network controller is brought forward by the paper to solve trajectory tracking problems of robotic manipulators with uncertainties. The first scheme consists of a PD feedback and a dynamic compensator which is composed by neural network controller and variable structure controller. Neutral network controller is designed to adaptive learn and compensate the unknown uncertainties, variable structure controller is designed to eliminate approach errors of neutral network. The adaptive weight learning algorithm of neural network is designed to ensure online real-time adjustment, offline learning phase is not need; Global asymptotic stability (GAS) of system base on Lyapunov theory is analysised to ensure the convergence of the algorithm. The simulation result s show that the kind of the control scheme is effective and has good robustness.
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