Abstract
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 non-conventional methods based on Genetic Algorithms (GAs) and the third
method is a conventional method which is a 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.
