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Intelligent control using multiple neural networks
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DOI：10.1002/acs.759.png)
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Abstract
En 中文
In this paper a framework for intelligent control is established to adaptively control a class of non-linear discrete-time dynamical-systems while assuring boundedness of signals. A linear robust adaptive controller and multiple non-linear neural network based adaptive controllers are used, and a switching law is suitably defined to switch between them, based upon their performances in predicting the plant output. Boundedness of signals is established with minimum requirements on the parameter adjustment mechanisms of the neural network controllers, and thus the latter can be used in novel ways to better detect changes in the system being controlled, and to initiate fast adaptation. Simulation studies show the effectiveness of the proposed approach. Copyright (C) 2003 John Wiley Sons, Ltd.
Keywords:
intelligent control
non-linear control
adaptive control
neural networks
multiple models
switching systems
stability
robustness
performance
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