Towards achieving an optimum speed performance for DC servo motors via Fuzzy logic controllers

Abstract

DC motors have been intensively implemented and used for many industrial tasks starting from small articulated robots with different degrees of freedom (DOFs), domestic electric appliances and not ending with electric vehicles and trains. The main role of these motors is to provide a desired positioning and velocity performance for the mechanical system via pre-designed control techniques. However, achieving an optimum speed performance for servo motors is not an easy task, due to the influences of the inner and outer loads on their output stability. Nowadays, many researchers have applied different control techniques in order to stabilize the speed performance such as via popular Ziegler-Nichols methods, particle swarm optimization, Genetic algorithms, neural networks and fuzzy logic controllers. The late optimization technique will be used in this work for controlling the speed of a specific DC servo motor (namely CE110), the performance of the Fuzzy logic controller will be examined when the motor is operating under different load conditions, which are the varying inner loads caused by the moment of inertia and the changing outer loads resulted from the generator. Moreover, the CE110 motor system was mathematically modeled and its performance was investigated when different PID control forms are used. The results showed the capability of the fuzzy logic controller to provide an excellent performance under varying load conditions compared with PID controllers based on Cohen Coon ZN tuning formula.