Intelligent Speed Control of Hybrid Stepper Motor Considering Model Uncertainty Using Brain Emotional Learning

Abstract

This paper presents an implementation of the brain emotional learning-based intelligent controller (BELBIC) for precise speed tracking of the hybrid stepper motor (HSM). Such a configuration is applicable where high resolution and accuracy is essential particularly in uncertain conditions. The proposed controller is a model-free controller independent of the model dynamics and variations that occur in a system. It is capable of autolearning to handle unforeseen disturbances. To evaluate the performance of the BELBIC controller in realistic conditions, the uncertainty of the system as a result of mechanical parameter variation and load torque disturbance is considered. To verify an excellent dynamic performance and the feasibility of the BELBIC, the system is simulated in MATLAB Simulink, and the results of the simulation are compared with an optimized proportional integral (PI) controller. The simulation results confirm the superior performance of the BELBIC for fast and precise speed response as well as its potential in dealing with nonlinearity and uncertainty handling as compared with that of the PI controller. The proposed controller is used in realistic applications, such as tunable-laser system and robot-assisted surgery.