Neural PCA Controller based on Multi-Models



In this paper, a new approach to design nonlinear adaptive
PI multi-controllers, for SISO systems, based on neural local linear principal
components analysis (PCA) models is proposed. The PCA neural
networks only implements the integral term of the PI multi-controller, a
proportional term is added to obtain a PI structure. A modied normalized
Harris performance index is used for evaluating the controller performance.
Some experimental results obtained with a nonlinear three tank
benchmark model are presented, showing the adaptive PI-PCA multicontroller
performance compared to neural linear PI controllers.


nonlinear adaptive PI control, principal component analysis, multi-models.


11th Portuguese Conference on Automatic Control, June 2014

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