Constrained Nonlinear Multi-objective Predictive Control

This paper describes constrained multi objective predictive control of nonlinear systems. A nonlinear model based on the artificial neural networks (ANNs) is used to characterize the process at each operating point. The control law is provided by minimizing a set of control objective which is function of the future prediction output and the future control actions. Three aggregative methods are used to compute the control law. The first and the second methods are based on genetic algorithms (GAs) and the third method is found on the combination between the weighted sum method and the ellipsoid algorithm. The proposed control scheme is applied to a numerical example to illustrate the performance of the proposed predictive controller