TRAJECTORY TRACKING OF QUADROTOR UNMANNED AERIAL VEHICLE USING PARTICLE SWARM OPTIMIZATION BASED ADAPTIVE SUPERTWISTING SLIDING MODE CONTROL. .
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Date
2026-05-20
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Abstract
Quadrotor Unmanned Aerial Vehicles are increasingly used in search and res-cue, agriculture, mapping, service delivery, military surveillance, and power line inspection. However, their underactuated dynamics, coupled with parametric un certainties,unmodelled dynamics, and disturbances pose substantial challengesfor robust trajectory tracking and attitude stabilization.Among othersclassical controllers including Proportional Integral Derivatives, Model Predictive Control and Linear Quadratic Regulator offer acceptable perfor-mance but lack robustness under disturbances, and parameter variations. Sliding Mode Control overcomes key limitations of linear controllers with strong robust-ness, however, its main weakness is chattering caused by discontinuous switchingcontrol, which may excite unmodeled dynamics and wear actuator.This work develops a quaternion based dynamic model for quadrotor motion that captures both translational and rotational behavior. Unlike many existing models, the proposed formulation includes unmodeled effects such as aerodynamicdrag and propeller induced forces, resulting in a more realistic and more accurate representation of quadcopter flight dynamics. An Adaptive Super Twisting Sliding Mode Controller (ASTSMC) based on quater- nion modeling was designed such that quaternion representation ensures smooth attitude tracking without gimbal lock, while Super Twisting reduces chattering and improves robustness.M Particle Swarm Optimization (PSO) was used to tune the sliding surface gains of the ASTSMC, reducing attitude objective values by 4.43%–27.22% with respectto initial global best value and improving position accuracy by up to 10.62%. The Best Global value drop from 10.49 to 7.66 reflects a 26.97% reduction in combined tracking error and control effort. Gains tuned by PSO yield faster convergence, smoother response, and better performance than manual tuning. The proposed PSO ASTSMC significantly enhances quadrotor trajectory track-ing by reducing attitude errors by up to 27% and improving position accuracy by 0.61%–10.62% relative to a conventional PID and backstepping sliding mode control under identical simulation conditions.
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This thesisi was prepared by MUSA DAUD