Multi-objective genetic optimisation of GPC and SOFLC tuning parameters using a fuzzy-based ranking method

A multi-objective genetic algorithm is developed for optimising the tuning parameters relating to the generalised predictive control (GPC) and perform ance index table of the self-organising fuzzy logic (SOFLC) algorithms, usi ng a multi-objective ranking method based on fuzzy logic theory. A comparat ive study with more traditional pareto, average and minimum distance rankin g methods shows that the proposed method is superior. The study shows that the approach leads to a more effective set of tuning parameters, especially those relating to the important observer polynomial for GPC and to a good reference trajectory for SOFLC. Up to two objective functions were used in the study, although the method can be extended to more objectives. A nonlin ear muscle-relaxant anaesthesia model is used as a case study to demonstrat e the robustness of the method.