Authors : Mama Behera; Snigdha Nayak; Droupadi Nayak; Reenupama Nayak

Volume/Issue : Volume 11 - 2026, Issue 5 - May

Google Scholar : https://tinyurl.com/zk3cytak

Scribd : https://tinyurl.com/39dht4bt

DOI : https://doi.org/10.38124/ijisrt/26May130

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This study presents a systematic investigation of Particle Swarm Optimization (PSO)-based tuning of classical controllers for aircraft longitudinal control, with explicit comparison against conventionally tuned baseline controllers. A linearized four-state aircraft model representing short-period and phugoid dynamics is considered, where pitch angle is selected as the controlled output. Four controller structures, namely P, PI, PD, and PID, are tuned using both classical heuristic methods (baseline) and PSO under a unified multi-objective fitness framework. The objective function integrates Integral of Squared Error (ISE), Integral of Time-weighted Absolute Error (ITAE), and control effort, ensuring balanced transient and steady-state performance. The baseline controllers are designed using standard tuning principles, serving as a reference for performance evaluation. In contrast, PSO employs adaptive inertia weight and bounded gain search spaces to achieve global optimization. Comparative analysis is carried out using step response, control effort, and disturbance rejection characteristics. Key performance indices such as rise time, settling time, overshoot, and steady-state error are evaluated for both baseline and optimized cases. Results clearly indicate that PSO-tuned controllers significantly outperform baseline designs across all metrics. The PID controller exhibits the best overall performance, achieving faster settling, reduced overshoot, and negligible steady-state error compared to its baseline counterpart. PD control demonstrates improved damping with lower control effort, while PI shows moderate improvement in steady-state accuracy. The baseline P controller remains inadequate for stringent performance requirements. The findings validate that PSO provides a robust and efficient optimization framework for controller tuning in aerospace systems, enabling superior dynamic performance over traditional design approaches.

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