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Multiobjective optimization of a fuzzy PID controller

Soukkou A., Khellaf A. H., Leulmi S.

Archives of Control Sciences

2006

Vol. 16, no. 4

445-461

A fuzzy logic controller with multilayer neutral network whose synaptic weights represent the fuzzy knowledge base and its application to the highly nonlinear systems is presented in this work. The scaling factors of the input variables, membership functions and the rule sets are optimized by the use of the multiobjective genetic algorithms. The fuzzy network structure is specified by a combination of the mixed Takagi-Sugeno's and Mamdani's fuzzy reasoning. The mixed, Binary-Real-Integer, optimal coding is utilized to construct the chromosomes, which define the same of necessary prevai;ling parameters for the conception of the desired controller. This new controller stands out by a non-standard gain, which varies lineary with the fazzy inputs. Under certain conditions, it becomes similar to the conventional PID controller with non-linearly variable coefficients. Computer simulation indicates that the designed fuzzy controller is satisfactory in control of a nonlinear system "Inverted Pendulum".