@article{MAKHILLIJSC20072120795,
    title = {Constrained Nonlinear Neural Model Based Predictive Control Using Genetic Algorithms},
    journal = {International Journal of Soft Computing},
    volume = {2},
    number = {1},
    pages = {69-74},
    year = {2007},
    issn = {1816-9503},
    doi = {ijscomp.2007.69.74},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9503&doi=ijscomp.2007.69.74},
    author = {Mohamed Boumehraz},
    keywords = {Nonlinear system,model,predictive control,neural network,genetic algorithm},
    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.}
    }