Dissimilarity Clustering Algorithm for Designing the PID-like Fuzzy Controllers

Abstract

Fuzzy logic controller is one of the most prominent research fields to improve efficiency for process industries, which usually stick to the conventional proportional-integral-derivative (PID) control. The paper proposes an improved version of the three-term PID-like fuzzy logic controller by removing the necessity of having user-defined parameters in place for the algorithm to work. The resulting non-parametric three-term dissimilarity-based clustering fuzzy logic controller algorithm was shown to be very efficient and fast. The performance study was conducted by simulation on armature-controlled and field-controller DC motors, for linguistic type and Takagi-Sugeno-Kang (TSK) models. Comparison of the created algorithm with fuzzy c-means algorithm resulted in improved accuracy, increased speed and enhanced robustness, with an especially high increase for the TSK type model.