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Fuzzy Controller Design Using Evolutionary & Swarm Algorithms

Fuzzy controllers, as nonlinear controllers, represent successful implementation of fuzzy logic in practical control problems. These controllers, based on fuzzy logic, simulate the behaviour of experts in controlling of systems. Unlike their classical counterparts, fuzzy controllers do not require a precise mathematical model of the systems and using the expert knowledge, they construct their rule base.


One of the disadvantages of fuzzy controllers is the lack of the learning ability. Hence they are strongly dependent on the expert knowledge. A learning strategy can cope with this problem and automate the controller design process through adding the ability of learning and modifying controller parameters.


In this research the problem of designing a fuzzy controller is stated in the form of an optimization problem. The optimal fuzzy controller is determined through finding the membership function of the variables, fuzzy rules, number of fuzzy rules, t-norm operator, s-norm operator and deffuzification operator, using evolutionary and swarm Algorithms.


For this purpose, two evolutionary algorithms including Genetic Algorithm (GA) and Cooperative Genetic Algorithm and two swarm algorithms including Particle Swarm Optimization (PSO) and Imperialist Competitive Algorithm (ICA) are implemented.

  • Ashkan Jasour, A. Tamjidi, M. Vakili, ”Particle Swarm Optimization and Genetic Algorithm for Fuzzy Controller Design”, Second Iranian Joint Congress on Fuzzy and Intelligent Systems(IFIS), Tehran, Iran, October, 2008
  • Ashkan Jasour, ”Fuzzy Controller Design Using Genetic Algorithm”, 9th Iranian Student Confer- ence on Electrical Engineering (ISCEE), Tehran, Iran, September, 2006