SYSTEMATIC DESIGN PROCEDURE OF TS-TYPE FUZZY CONTROLLERS
Ammar Soukkou, Abdelghani Khellaf, Salah Leulmi · International Journal of Computational Intelligence and Applications · 2006
This paper contributes a new alternative for the synthesis of Takagi–Sugeno fuzzy logic controller with reduced rule base. A Genetic Approach to Fuzzy Supervised Learning algorithm called GAFSL based on the Multiobjective Genetic Algorithms (MGAs) is used to construct the proposed robust fuzzy controller. The result controller is similar to nonlinear PI/PD controllers. The tuning algorithm cannot only tune the scaling factors, the shapes of membership functions, and the consequent values, but also optimize the number of rules as possible with guaranteed desired performances: accuracy and robustness. The construction of the chromosomes is based on the mixed binary–real coding system. The genes of chromosome are arranged into two parts, the first part contains the control genes (the certainty factors) and the second part contains the parameters genes that represent the fuzzy knowledge base. The concept of elite strategy is adopted, where the best individuals in a population are regarded as elites. Computer simulation results on two nonlinear problems that are derived to demonstrate the powerful GAFSL algorithm.