Adaptive Control for Robotic Manipulators base on RBF Neural Network
Abstract: 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 results show that the kind of the control scheme is
effective and has good robustness.
Author: Ma Jing, Zhang Wenhui, Zhu Haiping
Journal Code: jptkomputergg130080
