Robust Neural Network Tracking Controller Using Simultaneous Perturbation Stochastic Approximation

@article{Song2008RobustNN,
  title={Robust Neural Network Tracking Controller Using Simultaneous Perturbation Stochastic Approximation},
  author={Q. Song and J. Spall and Y. Soh and J. Ni},
  journal={IEEE Transactions on Neural Networks},
  year={2008},
  volume={19},
  pages={817-835}
}
  • Q. Song, J. Spall, +1 author J. Ni
  • Published 2008
  • Computer Science, Medicine
  • IEEE Transactions on Neural Networks
  • This paper considers the design of robust neural network tracking controllers for nonlinear systems. The neural network is used in the closed-loop system to estimate the nonlinear system function. We introduce the conic sector theory to establish a robust neural control system, with guaranteed boundedness for both the input/output (I/O) signals and the weights of the neural network. The neural network is trained by the simultaneous perturbation stochastic approximation (SPSA) method instead of… CONTINUE READING
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