Multi-criteria optimization in nonlinear predictive control

The multi-criteria predictive control of nonlinear dynamical systems based on Artificial Neural Networks (ANNs) and genetic algorithms (GAs) are considered. The (ANNs) are used to determine process models at each operating level; the control action is provided by minimizing a set of control objective which is function of the future prediction output and the future control actions in tacking in account constraints in input signal. An aggregative method based on the Non-dominated Sorting Genetic Algorithm (NSGA) is applied to solve the multi-criteria optimization problem. The results obtained with the proposed control scheme are compared in simulation to those obtained with the multi-model control approach.