ROBUST ADAPTIVE CONTROL OF ROBOTS USING NEURAL NETWORK : GLOBAL STABILITY

@inproceedings{Kwan2002ROBUSTAC,
  title={ROBUST ADAPTIVE CONTROL OF ROBOTS USING NEURAL NETWORK : GLOBAL STABILITY},
  author={Cindy Kwan and Darren M. Dawson},
  year={2002}
}
A desired compensation adaptive law-based neural network (DCALNN) controller is proposed for the robust position control of rigid-link robots. The NN is used to approximate a highly nonlinear function. The controller can guarantee the global asymptotic stability of tracking errors and boundedness of NN weights. In addition, the NN weights here are tuned on-line, with no offline learning phase required. When compared with standard adaptive robot controllers, we do not require linearity in the… CONTINUE READING
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