Tuning GPC using a new fuzzy-based ranking method for multi-objective genetic optimisation

A multi-objective genetic algorithm is developed for the purpose of optimising the tuning parameters relating to the well-known Generalised Predictive Control (GPC) algorithm using a new ranking method based on Fuzzy Logic theory. A comparative study with the well-known Pareto-Ranking, Average, and Minimum Distance methods shows that the new proposed method is superior and leads to a more effective set of tuning parameters, especially those relating to the important observer polynomial T (z^-1). Up to two objective-functions were used in this study, although the method can be extended to a higher number of objectives. A non-linear muscle relaxant anaesthesia model is used as a case study to demonstrate the robustness of this new method.