Neural AILC for Error Tracking Against Arbitrary Initial Shifts

@article{Sun2018NeuralAF,
  title={Neural AILC for Error Tracking Against Arbitrary Initial Shifts},
  author={Mingxuan Sun and Tao Wu and Lejian Chen and Guofeng Zhang},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2018},
  volume={29},
  pages={2705-2716}
}
This paper concerns with the adaptive iterative learning control using neural networks for systems performing repetitive tasks over a finite time interval. Two standing issues of such iterative learning control processes are addressed: one is the initial condition problem and the other is that related to the approximation error. Instead of the state tracking, an error tracking approach is proposed to tackle the problem arising from arbitrary initial shifts. The desired error trajectory is… CONTINUE READING
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