An Improved Tuning of PID Controller for PV Battery-Powered Brushless DC Motor Speed Regulation Using Hybrid Horse Herd Particle Swarm Optimization

In this study, speed control of PV battery-powered brushless DC motor (BLDC) is controlled by novel hybrid horse herd particle swarm optimization- (HHHPSO-) tuned proportional integral derivative (PID) controller. The optimal gain parameter of the PID controller is tuned by hybrid horse herd optimization algorithm. The purpose of the newly developed HHHPSO algorithm is to enhance the performance of the classic horse herd algorithm (HHA), specifically in two different ways. In the first place, it bolsters HHA’s aptitude for exploratory learning related to the ageing issue. By doing so, it is possible to circumvent the phenomenon of the local minimum stagnation. Second, it permits HHA to have a superior capability of exploitation with the assistance of hybridization through the utilisation of particle swarm optimization. This hybrid technique helps improve the rate convergences of the HHA method. The time domain-based performance indices were considered as an objective function such as addition of integral of squared speed error, integral of squared current error, and integral of squared electromagnetic torque error for finding the optimal gain values for the PID controller using HHHPSO. The proposed HHHPSO-tuned PID controller for PV battery-powered BLDC motor is tested for various working conditions such as constant speed conditions, varying speed conditions, and varying load conditions and also compared with state-of-the-art method. The proposed method has quick rise time around 20-21 msec, quick settling time around 35-39 msec, zero steady-state error, and zero overshoot than state-of-the-art optimization method. The proposed control techniques were also tested in hardware to confirm the suitability for real-time applications.