Abstract
The problem of controlling processes that operate within a wide range of operating conditions is addressed. The operating space is decomposed into a set of local model forming a network. These are combined into a global model structure using an interpolation method based on normalized basis functions. Unknown local model parameters are identified using empirical data. The control problem is solved using a model predictive controller based on the local model network (LMN). The van de Vusse reaction is used as example. The MPC based on LMN is compared with an exact nonlinear process model and others MPC algorithms based on linear models. Finally, several future works in this area are pointed out.
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Trierweiler, J.O., Secchi, A.R. (2000). Exploring the Potentiality of Using Multiple Model Approach in Nonlinear Model Predictive Control. In: Allgöwer, F., Zheng, A. (eds) Nonlinear Model Predictive Control. Progress in Systems and Control Theory, vol 26. Birkhäuser, Basel. https://doi.org/10.1007/978-3-0348-8407-5_11
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DOI: https://doi.org/10.1007/978-3-0348-8407-5_11
Publisher Name: Birkhäuser, Basel
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