Adaptive fractional order PID controller tuning for brushless DC motor using Artificial Bee Colony algorithm

This paper presents an adaptive Fractional Order PID (FOPID) controller for improving the performance of a Brushless DC (BLDC) motor using Artificial Bee Colony (ABC) algorithm. BLDC motor is desired to operate at various speed and load conditions with enhanced performance and robust speed control. In practice, the effect of longer settling time, fluctuation of steadystate error, power fluctuation and nonlinearity characteristics of the BLDC motor drive result in poor controllability. To overcome the problems, an optimized FOPID controller using the ABC algorithm in a self -tuned regulator structure is proposed to minimize the given objective function to satisfy the inequality constraints. It is also interesting to note that the usage of Hall Effect sensors has many limitations due to the failure of its components, poor reliability, need special mechanical arrangements for mounting and electrical noise aspects. In order to avoid such issues, a Kalman Filter is designed for estimating the speed of the motor. The simulation carried out for the proposed ABC tuned FOPID controller and the results are compared with conventional genetic algorithm and modified genetic algorithm tuned FOPID controllers. The results indicate that the proposed ABC tuned controller is superior in terms of time -domain characteristics, control effort, and specified performance indices. Further to show the usefulness of the proposed method, an experimental model is developed and validated for the selected operating conditions with the required comparison.