Dynamic and Steady-State Analysis of Tilt Integral Derivative Controller for Sensorless Brushless DC Motor Drive using Hybrid Optimization Algorithms

Industrial automation and electric mobility applications extensively use Brushless Direct Current (BLDC) motors for high efficiency, rapid response, compact design, and higher power density. The main difficulty in achieving accurate speed control is the lack of flexibility and adaptability in traditional controllers, which limits performance in different load and operational conditions. The study proposed a Tilt Integral Derivative (TID) controller with Puffer Fish Optimization (PFO) and Harris Hawk Optimization (HHO). The proposed model minimizes time domain performance, including rise period, overshoot, settling period, and steady-state error. The tuning process uses a closed-loop control that includes the TID, BLDC motor model, and feedback mechanism. The simulation results show substantial improvements in both transient and steady-state performance when tested at reference speeds ranging from 500 rpm to 2000 rpm. The PFO-tuned TID controller exhibits superior transient performance at medium and high speeds (1000-2000 rpm). It achieves faster rise times and reduced settling times, and the HHO-based tuning provides competitive and improved steady-state accuracy. The PFO-TID controller at 1000 rpm reaches a settling time of 0.059 s, which is faster than the HHO-TID controller's 0.072 s. The PFO approach produces lower steady-state error at 500 rpm. TID controller improves BLDC motor speed control performance in different operational conditions. Meta-heuristic optimization methods play an important role in enhancing motor performance. The HHO-tuned TID controller delivers reliable performance across multiple speed intervals. The results show that swarm-based optimization methods effectively deliver precise and dependable motor control results for demanding high-performance applications.