Veuillez utiliser cette adresse pour citer ce document : http://hdl.handle.net/123456789/4244
Titre: Constrained Non-Linear Neural Model Based Predictive Control using Genetic Algorithms
Auteur(s): M. Boumehraz
K. Benmahammed
Date de publication: 25-nov-2014
Résumé: 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 an efficient optimization procedure. In this paper, a neural network is used as a non-linear prediction model of the plant. The optimisation 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 optimisation routine which is nonconvex 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 a simulation example
URI/URL: http://dspace.univ-biskra.dz:8080/jspui/handle/123456789/4244
Collection(s) :Communications Internationales

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