Dynamic Learning From Neural Control for Strict-Feedback Systems With Guaranteed Predefined Performance

@article{Wang2016DynamicLF,
  title={Dynamic Learning From Neural Control for Strict-Feedback Systems With Guaranteed Predefined Performance},
  author={Min Wang and Cong Wang and Peng Shi and Xiaoping Liu},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2016},
  volume={27},
  pages={2564-2576}
}
This paper focuses on dynamic learning from neural control for a class of nonlinear strict-feedback systems with predefined tracking performance attributes. To reduce the number of neural network (NN) approximators used and make the convergence of neural weights verified easily, state variables are introduced to transform the state-feedback control of the original strict-feedback systems into the output-feedback control of the system in the normal form. Then, using the output error… CONTINUE READING

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