TY  - JOUR
T1  - Constrained Nonlinear Neural Model Based Predictive Control Using Genetic Algorithms
AU - , Mohamed Boumehraz 
JO  - International Journal of Soft Computing
VL  - 2
IS  - 1
SP  - 69
EP  - 74
PY  - 2007
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2007.69.74
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2007.69.74
KW  - Nonlinear system
KW  -model
KW  -predictive control
KW  -neural network
KW  -genetic algorithm
AB  - 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.
ER  -