|Autors: Petrov, M. G., Ahmed, S. A., Taneva, A. M., Todorov, Y. V.|
Title: Fuzzy Model Predictive Control of a MIMO System
Keywords: Model Predictive Control, Optimal Control, Fuzzy Control, MIMO system
Abstract: In this paper Nonlinear Model Predictive Control (NMPC) is studied as a more applicable approach for optimal control of multivariable processes. A state-space representation of a Takagi-Sugeno type fuzzy-neural model is proposed as a predictive model. This type of model ensures easier description and direct computation of the gradient control vector during the predictive optimization task. The identification procedure relies on a two-step training algorithm, which is known in field of artificial neural networks. The proposed Fuzzy NMPC approach is studied by experimental simulations in Matlab/Simulink® environment in order to control the liquid levels in a multi tank system. The simulation results demonstrate that the main process variables have a good performance and the process control quality is satisfied.
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