Predictive control using feedback linearization based on dynamic neural modelsDeng, 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, https://doi.org/10.1109/ICSMC.2007.4413858 . Full text not archived in this repository. It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1109/ICSMC.2007.4413858 Abstract/SummaryThis 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.
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