Abstract
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 modified 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 multi-controller performance compared to neural linear PI controllers.
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Palma, L.B., Coito, F.V., Gil, P.S. (2015). Neural PCA Controller Based on Multi-Models. In: Moreira, A., Matos, A., Veiga, G. (eds) CONTROLO’2014 – Proceedings of the 11th Portuguese Conference on Automatic Control. Lecture Notes in Electrical Engineering, vol 321. Springer, Cham. https://doi.org/10.1007/978-3-319-10380-8_11
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DOI: https://doi.org/10.1007/978-3-319-10380-8_11
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