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.
|Title of host publication||IEEE International Conference on Systems, Man and Cybernetics (ISIC 2007)|
|Place of Publication||Piscataway|
|Pages||2716 - 2721|
|Publication status||Published - 2007|
- predictive control, neural networks, nonlinear predictive control , NETWORKS, SYSTEMS