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Stochastic neural adaptive control for nonlinear time varying systems based on Newton and gradient optimizations

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2 Author(s)
Ho, Tuan T. ; Adv. Syst. Res., Aurora, CO, USA ; Ho, Hai T.

The authors present a stochastic neural adaptive control algorithm for nonlinear time-varying systems. The implicit neural identification is derived based on the Newton optimization approach. Using the one-step-prediction quadratic performance index, the authors design a control law which in combination with the identification algorithm constitutes an effective neural adaptive control algorithm. The identification and control are robust and computationally efficient for real-time control systems design

Published in:

Decision and Control, 1992., Proceedings of the 31st IEEE Conference on

Date of Conference:

1992

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