|Autors: Petrov, M. G., Ahmed, S. A., Ichtev, A. K., Taneva, A. M.|
Title: Fuzzy–Neural Model Predictive Control of Multivariable Processes
Keywords: Fuzzy Model Predictive Control, Adaptive Control
Abstract: In this chapter, Nonlinear Model Predictive Control (NMPC) is studied as a more applicable approach for optimal control of multivariable processes. In general, a wide range of industrial processes are inherently nonlinear. For such nonlinear systems it is necessary to apply NMPC. Recently, several researchers have developed NMPC algorithms (Martinsen et al., 2004) that work with different types of nonlinear models. Some of these models use empirical data, such as artificial neural networks and fuzzy logic models. The model accuracy is very important in order to provide an efficient and adequate control action. Accurate nonlinear models based on soft computing (fuzzy and neural) techniques, are increasingly being used in model-based control (Mollov et al., 2004). On the other hand, the mathematical model type, which the modelling algorithm relies on, should be selected.The proposed techniques of fuzzy-neural MPC are studied by experimental simulations in Matlab® environment.
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