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International Journal of Soft Computing
Year: 2007  |  Volume: 2  |  Issue: 1  |  Page No.: 69 - 74

Constrained Nonlinear Neural Model Based Predictive Control Using Genetic Algorithms

Mohamed Boumehraz    

Abstract: Nonlinear Model Based Predictive Control (MBPC) is one of the most powerful techniques in process control, however, two main problems need to be considered; obtaining a suitable nonlinear model and using an efficient optimization procedure. In this study, a neural network is used as a non-linear prediction model of the plant. The optimization routine is based on Genetic Algorithms (GAs). First a neural model of the non-linear system is derived from input-output data. Next, the neural model is used in an MBPC structure where the critical element is the constrained optimization routine which is no convex and thus difficult to solve. A genetic algorithm based approach is proposed to deal with this problem. The efficiency of this approach had been demonstrated with simulation examples.

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