Natural Exponential Inertia Weight and Acceleration Coefficient Particle Swarm Optimization Algorithm tuned PID Controller for DC Motor Speed Control. - Adu-Buabeng , Dominic; Sekyere, Yaw Opoku Mensah; Effah, Francis Boafo (JURNAL NASIONAL TEKNIK ELEKTRO, 2025)
This paper presents a novel optimization algorithm, the NExIWAC (Natural Exponential Inertia Weight and Acceleration Coefficient) variant of Particle Swarm Optimization (PSO), for tuning PID controllers in DC motor speed control systems. The proposed NExIWAC algorithm improves control performance by dynamically adjusting the inertia weight and acceleration coefficients during optimization. To evaluate its effectiveness, the NExIWAC-tuned PID controller was compared against five established metaheuristic algorithms: Atomic Search Optimization (ASO), Sand Cat Swarm Optimization (SCSO), Grey Wolf Optimization (GWO), Invasive Weed Optimization (IWO), and Stochastic Fractal Search (SFS). The system's step response was analyzed under a reference speed demand of 1 p.u., with performance metrics including steady-state error, rise time, settling time, overshoot, and Integral of Time-weighted Absolute Error (ITAE). The NExIWAC algorithm demonstrated superior performance, achieving the fastest rise and settling times, zero steady-state error, and the lowest ITAE value among the tested algorithms. A robustness analysis was conducted by varying motor parameters, such as armature resistance and motor constant, by ±50%. The NExIWAC-PID controller exhibited stable and reliable performance under all conditions. Stability analysis through Bode plots and pole-zero mapping further confirmed the system's robust behavior, with a high phase margin and poles located in the left half of the complex plane. The results indicate that the NExIWAC algorithm is a powerful and reliable optimization tool for tuning PID controllers in DC motor applications, offering significant advantages in terms of precision, stability, and adaptability.