Pi controller for siso linear systems based on neural linear pca



In this paper an approach to design proportionalintegral
(PI) controllers, for SISO systems, based on neural linear
principal components analysis (PCA) is presented. Closedloop
control can be formulated and implemented within the
reduced space defined by a PCA model. The neural linear PCA
controller, results in an integral controller, which can be used
as an inferential controller. The main contributions of the paper
are: a) the proposed architecture with a classical proportional
controller and a neural integral controller based on linear
neural PCA; b) the evaluation of the controller performance
using the Harris index. Some experimental results obtained with
a DC motor linear model are presented, showing the controller


PI Control, Principal components analysis, Harris index


13th European Control Conference (ECC), June 2014

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