BLDC Motor Control Optimization Using Optimal Adaptive PI Algorithm

Abstract

The main problem of using a Proportional Integral (PI) Controller in Brushless Direct Current (BLDC) motor speed control is tuning the PI’s parameter and its performance cannot adapt to the system behavior changes. Particle Swarm Optimization (PSO) has been chosen to optimize the tuning. Fuzzy Logic Controller (FLC) is used to online tuning PI’s parameters to adapt to system conditions. Optimal adaptive PI, which combines the PSO method and FLC method to tune PI, is proposed. It was successfully implemented in the simulation environment. The test was carried out in three conditions: step responses, set-point changes, and disturbance rejection. The proposed algorithm is superior with no overshoot/undershoot. Whereas in terms of settling time is in between PI and PI-PSO. PI controller has the smallest control effort. However, the other parameter is the worst. PI-PSO is superior in terms of settling time and Integral of Absolute Error (IAE) except for the step response test. The proposed method has lower IAE and higher control effort by 78.73 % and 60 % compared to PI control. On the other hand, it has a higher IAE dan lower control effort by 11.82 % and 33.88 % compared to PI-PSO. Therefore, the optimal adaptive PI control can reduce energy consumption compared to optimal PI with better performance than PI control.