Genetic algorithm optimization of I/O scales for FLIC in servomotor control

Abstract

Direct current (DC) servomotors are widely used in robot manipulator applications. Servomotors use feedback controller to control either the speed or the position or both. This paper discusses the modeling and simulation of DC servomotor control built using MATLAB/Simulink, and the analysis of controller performance, namely a Fuzzy Logic parallel I Controller (FLIC) in which the I/O scale factors of Fuzzy Logic Controller (FLC) and integrator constant are optimized using Genetic Algorithm (GA). The singleton fuzzification is used as a fuzzifier: seven membership functions for both input and output of fuzzy logic controller. The center average is used as a defuzzifier. The 32-bit-50-population is used in GA. Two control modes are applied in cascade to the plant: speed control in the position control loop. Simulation results show that FLIC with GA-optimized is the best performance compared to FLIC without GA and conventional FLC for the speed and position control of DC servomotor.