Genetic and ellipsoid algorithms for nonlinear predictive control

This paper deals with the constrained predictive control of nonlinear systems. Artificial Neural Networks (ANN) are used as a process model. The control law is derived by minimizing a non convex criterion. The optimization problem is solved using Ellipsoid and genetic algorithms. The structure and operators of the combining two algorithms have been specifically developed for control design problem. Simulation results are presented to illustrate the performances of the proposed predictive controller.