Using genetic algorithms to optimize the design parameters of generalized predictive controllers

Generalized predictive control algorithms are a powerful control design method widely applied to industrial processes. However, there is no easy way to tune the design parameters, mainly prediction and control horizons, in order to achieve optimal performance. In this work, a genetic algorithm is proposed whose aim is to find on line the optimal values for the tuning parameters.