Mohamed Boumehraz  , 
    Constrained Nonlinear Neural Model Based Predictive Control Using Genetic Algorithms,
    International Journal of Soft Computing,
    Volume 2,Issue 1,
    2007,
    Pages 69-74,
    ISSN 1816-9503,
    ijscomp.2007.69.74,
    (https://makhillpublications.co/view-article.php?doi=ijscomp.2007.69.74)
    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.
    Keywords: Nonlinear system;model;predictive control;neural network;genetic algorithm