Back-propagation neural networks for nonlinear self-tuning adaptive control
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A back-propagation neural network is applied to a nonlinear self-tuning tracking problem. Traditional self-tuning adaptive control techniques can only deal with linear systems or some special nonlinear systems. The emerging back-propagation neural networks have the capability to learn arbitrary nonlinearity and show great potential for adaptive control applications. A scheme for combining back-propagation neural networks with self-tuning adaptive control techniques is proposed, and the control mechanism is analyzed. Simulation results show that the new self-tuning scheme can deal with a large unknown nonlinearity.<<ETX>>
[1] Job van Amerongen,et al. Adaptive steering of ships - A model reference approach , 1982, Autom..
[2] Ian Postlethwaite,et al. H∞-optimal design for helicopter control , 1988, 1988 American Control Conference.
[3] Jean-Jacques E. Slotine,et al. Neural Network Control of Unknown Nonlinear Systems , 1989, 1989 American Control Conference.
[4] Robert Hecht-Nielsen,et al. Theory of the backpropagation neural network , 1989, International 1989 Joint Conference on Neural Networks.