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Predictive control using feedback linearization based on dynamic neural models

Deng, J. M., Becerra, V.M. and Stobart, R. (2007) Predictive control using feedback linearization based on dynamic neural models. In: IEEE International Conference on Systems, Man and Cybernetics (ISIC 2007), Montreal, Canada.

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To link to this item DOI: 10.1109/ICSMC.2007.4413858

Abstract/Summary

This paper presents a hybrid control strategy integrating dynamic neural networks and feedback linearization into a predictive control scheme. Feedback linearization is an important nonlinear control technique which transforms a nonlinear system into a linear system using nonlinear transformations and a model of the plant. In this work, empirical models based on dynamic neural networks have been employed. Dynamic neural networks are mathematical structures described by differential equations, which can be trained to approximate general nonlinear systems. A case study based on a mixing process is presented.

Item Type:Conference or Workshop Item (Paper)
Divisions:Faculty of Science
ID Code:14645
Uncontrolled Keywords:predictive control, neural networks, nonlinear predictive control , NETWORKS, SYSTEMS
Publisher:IEEE

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