Journal
NEUROCOMPUTING
Volume 69, Issue 16-18, Pages 1868-1881Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.neucom.2006.02.017
Keywords
multiple controllers; learning models; neural networks; PID control; zero-pole placement control; switching
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A new adaptive multiple-controller is proposed incorporating a radial basis function (RBF) neural network based generalized learning model (GLM). The GLM assumes that the unknown complex plant is represented by an equivalent stochastic model consisting of a linear time-varying sub-model plus a non-linear RBF neural-network learning sub-model. The proposed non-linear multiple-controller methodology provides the designer with a choice, through simple switching, of using: either, a conventional proportional-integral-derivative (PID) controller, a PID structure based pole (only) placement controller, or a newly developed PID structure based (simultaneous) zero and pole placement controller. Closed-loop stability analysis of the multiple-controller framework is discussed and sample simulation results using a realistic non-linear single-input single-output (SISO) plant model are used to demonstrate the effectiveness of the multiple-controller with respect to tracking desired set-point changes and dealing with sudden introduction of disturbances. (c) 2006 Published by Elsevier B.V.
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