Phasor particle swarm optimization-based control for Quadcopter systems: An event-triggered impulsive super twisting approach

Abstract

Achieving precise and robust trajectory tracking in quadcopter systems remains a significant challenge due to inherent system nonlinearities, external disturbances, and actuator constraints. Conventional control strategies, such as classical sliding mode control (SMC) and proportional–integral–derivative (PID) controllers, often suffer from high-frequency chattering and limited adaptability to dynamic environments. To address these limitations, this study presents a novel control framework that synergistically integrates event-triggered impulsive super twisting terminal sliding mode control (ETISTT-SMC) with phasor particle swarm optimization (PPSO). The proposed approach enhances control precision while mitigating chattering effects, offering superior performance over traditional techniques. Unlike conventional SMC-based methods that rely on fixed control gains, ETISTT-SMC introduces event-triggered and impulsive control mechanisms, reducing unnecessary control updates and enhancing computational efficiency. PPSO, a refined swarm intelligence optimization algorithm, is employed to dynamically tune the controller parameters, ensuring optimal performance across varying flight conditions. This integration enables the quadcopter to achieve improved transient response, higher tracking accuracy, and greater robustness against disturbances. Extensive numerical simulations validate the efficacy of the proposed framework. Comparative analyses demonstrate that ETISTT-SMC achieves exceptional yaw stabilization, while SMC-PPSO and ETISTT-PPSO provide smoother and more responsive roll and pitch control. Furthermore, Lyapunov stability analysis rigorously establishes system stability under diverse operational conditions. Evaluations on complex 3D trajectory scenarios further confirm the robustness of the proposed methodology. Beyond theoretical and simulation-based validation, the modularity and adaptability of the control framework make it well-suited for real-world applications, including autonomous aerial surveillance, precision agriculture, disaster response, and infrastructure inspection. This research lays a strong foundation for future experimental validation and real-world deployment, paving the way for more efficient and resilient UAV control systems.