Optimization of fuzzy controller design using a Differential Evolution algorithm with dynamic parameter adaptation based on Type-1 and Interval Type-2 fuzzy systems

This paper proposes the use of the Differential Evolution algorithm with fuzzy logic for parameter adaptation in the optimal design of fuzzy controllers for nonlinear plants. The Differential Evolution algorithm is enhanced using Type-1 and Interval Type-2 fuzzy systems for achieving dynamic adaptation of the mutation parameter. In this paper, four control optimization problems in which the Differential Evolution algorithm optimizes the membership functions of the fuzzy controllers are presented. First, the experiments were performed with the original algorithm, second the experiments were performed with the Fuzzy Differential Evolution (in this case the mutation parameter is dynamic), and last, experiments were performed applying noise to the control plant by using Fuzzy Differential Evolution.