International Journal of Soft Computing
ISSN: OnlineISSN: Print 1816-9503
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.
How to cite this article:
Mohamed Boumehraz . Constrained Nonlinear Neural Model Based Predictive Control Using Genetic Algorithms.
DOI: https://doi.org/10.36478/ijscomp.2007.69.74
URL: https://www.makhillpublications.co/view-article/1816-9503/ijscomp.2007.69.74