Direct adaptive iterative learning control of nonlinear systems using an output-recurrent fuzzy neural network

@article{Wang2004DirectAI,
  title={Direct adaptive iterative learning control of nonlinear systems using an output-recurrent fuzzy neural network},
  author={Ying-Chung Wang and Chiang-Ju Chien and Ching-Cheng Teng},
  journal={IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)},
  year={2004},
  volume={34},
  pages={1348-1359}
}
In this paper, a direct adaptive iterative learning control (DAILC) based on a new output-recurrent fuzzy neural network (ORFNN) is presented for a class of repeatable nonlinear systems with unknown nonlinearities and variable initial resetting errors. In order to overcome the design difficulty due to initial state errors at the beginning of each iteration, a concept of time-varying boundary layer is employed to construct an error equation. The learning controller is then designed by using the… CONTINUE READING
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A Course in Fuzzy Systems and Control

  • L. X. Wang
  • Englewood Cliffs, NJ: Prentice-Hall,
  • 1997
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