Asymptotic Tracking for Uncertain Dynamic Systems Via a Multilayer Neural Network Feedforward and RISE Feedback Control Structure

@article{Patre2008AsymptoticTF,
  title={Asymptotic Tracking for Uncertain Dynamic Systems Via a Multilayer Neural Network Feedforward and RISE Feedback Control Structure},
  author={Parag M. Patre and William MacKunis and M. Kent Kaiser and Warren E. Dixon},
  journal={IEEE Transactions on Automatic Control},
  year={2008},
  volume={53},
  pages={2180-2185}
}
The use of a neural network (NN) as a feedforward control element to compensate for nonlinear system uncertainties has been investigated for over a decade. Typical NN-based controllers yield uniformly ultimately bounded (UUB) stability results due to residual functional reconstruction inaccuracies and an inability to compensate for some system disturbances. Several researchers have proposed discontinuous feedback controllers (e.g., variable structure or sliding mode controllers) to reject the… CONTINUE READING
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References

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Showing 1-10 of 18 references

Nonlinear network structures for feedback control

F. L. Lewis
Asian J. Control, vol. 1, no. 4, pp. 205–228, 1999. • 1999
View 4 Excerpts
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Robust adaptive control of robots using neural network: Global tracking stability

C. M. Kwan, D. M. Dawson, F. L. Lewis
Proc. IEEE Conf. Decision and Control, New Orleans, LA, 1995, pp. 1846–1851. • 1995
View 3 Excerpts
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A Continuous Control Mechanism for Uncertain Nonlinear Systems in Optimal Control, Stabilization, and Nonsmooth Analysis

B. Xian, M. S. de Queiroz, D. M. Dawson
2004
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