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

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3 Author(s)
Mahfouf, M. ; Dept. of Autom. Control & Syst. Eng., Sheffield Univ., UK ; Linkens, D.A. ; Abbod, M.F.

A multi-objective genetic algorithm is developed for optimising the tuning parameters relating to the generalised predictive control (GPC) and performance index table of the self-organising fuzzy logic (SOFLC) algorithms, using a multi-objective ranking method based on fuzzy logic theory. A comparative study with more traditional Pareto, average and minimum distance ranking 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 nonlinear muscle-relaxant anaesthesia model is used as a case study to demonstrate the robustness of the method

Published in:

Control Theory and Applications, IEE Proceedings -  (Volume:147 ,  Issue: 3 )