Genetic Algorithm optimization of I/O scales and parameters 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 Integral Controller (FLIC) in which the I/O scale factors, membership functions, and rules of Fuzzy Logic Controller (FLC) and integrator constant are optimized using Genetic Algorithm (GA) sequentially. The singleton fuzzification is used as a fuzzifier: seven membership functions initially 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 for I/O scales, and 21-bit-30-population is used in GA for membership functions. Two control modes are applied in cascaded to the plant: position control and speed control . Simulation results show that FLIC with GA-optimized is the best performance compared to FLIC, FLC, and FLC with GA.