Authors : Sarjen Murmu; Saraswati Kalundia; Munimai Lugun; Bhima Soren

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/3565rz5w

Scribd : https://tinyurl.com/f9yudupz

DOI : https://doi.org/10.38124/ijisrt/26apr1708

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Abstract : The proportional–integral–derivative (PID) controller remains dominant in DC motor drive applications due to its structural simplicity and ease of implementation. However, fixed-gain PID tuning methods degrade significantly under parametric uncertainty and nonlinear operating conditions. This work presents a robust particle swarm optimization (PSO)–based PID tuning framework incorporating Monte Carlo–driven uncertainty modeling and adaptive swarm dynamics. The DC motor parameters are subjected to independent ±30% variation to emulate realistic disturbances arising from thermal effects, load fluctuation, and frictional nonlinearity. The controller gains are optimized using a multiobjective cost function integrating time-weighted absolute error (ITAE), control effort, and overshoot penalty. A Monte Carlo evaluation loop is embedded within the PSO fitness computation, ensuring statistical robustness rather than nominal performance optimization. An adaptive inertia weight strategy is implemented to improve convergence characteristics and avoid premature stagnation. The proposed method is evaluated over 100 randomized parameter realizations, and performance is quantified using mean, standard deviation, and worst-case indices. Results indicate that conventional PSO-tuned PID controllers exhibit significant performance dispersion under uncertainty, whereas the proposed approach maintains consistent transient response and bounded control effort. The variance in performance metrics is reduced substantially, indicating improved robustness. The framework provides a scalable approach for uncertainty-aware controller design and is suitable for real-time extensions in intelligent drive systems.

Keywords : PID Controller; Particle Swarm Optimization; DC Motor; Monte Carlo Simulation; Parametric Uncertainty; Robust Control; Adaptive PSO.

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