Real-Time Implementation and Performance Evaluation of Brain Emotional Learning Developed for FPGA-Based PMBLDC Motor Drives

Much research is taking place in the field of motor drives, especially in the area of permanent magnet brushless DC motors (PMBLDC).This paper dealt with developing a Simulink model for PMBLDC motor drive systems with a high-performance, improved brain emotional-learning based intelligent controller (IBELBIC) using MATLAB/Simulink to control the speed and torque of the machine. To enhance the performance of PMBLDC motor, two control loops were employed for control of current and speed, respectively. Though the conventional PI controller may be used to control the speed of PMBLDC motor, the stability of the machine under changing loads cannot be ensured. Therefore, IBELBIC with a tracking system for the speed loop is proposed in this paper. It has been ascertained for the first time that the speed and torque performance of the system is enhanced by IBELBIC with minimum processing time. The contribution of this novel IBELBIC in improving the control system performance is shown by comparison with results obtained from classic PI controller, fuzzy PI controller, anti-windup PI controller, and IBELBIC. The results suggested that IBELBIC-based PMBLDC motor drive was more versatile for variable speed applications. The validation of the simulation was provided by a 400 W PMBLDC motor and drive system using Spartan 3 FPGA (Field Programmable Gate Array) based prototype, which is generally suitable for dedicated implementations of design and ease of redesign. The LABVIEW-based customized program was developed on a computer to monitor the motor performance.