Trajectory Tracking of Quadrotor UAVs Using Particle Swarm Optimized Adaptive Super Twisting Sliding Mode Control

Musa Daud; Jackson Githu Njiri

doi:10.48084/etasr.17726

Authors

Musa Daud Department of Mechatronic Engineering, Pan African University Institute for Basic Sciences, Technology and Innovation, Nairobi, Kenya | Department of Mechanical and Industrial Engineering, Mbeya University of Science and Technology, Mbeya, Tanzania
Jackson Githu Njiri Department of Mechatronic Engineering, Jommo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

Volume: 16 | Issue: 3 | Pages: 34807-34816 | June 2026 | https://doi.org/10.48084/etasr.17726

Received: 23 January 2026 | Revised: 22 February 2026 and 4 March 2026 | Accepted: 6 March 2026 | Online: 2 April 2026

Corresponding author: Musa Daud

Abstract

Quadrotor Unmanned Aerial Vehicles (UAVs) are increasingly used in sectors such as search and rescue, crop monitoring, pesticide spraying, aerial photography, service delivery, military surveillance, and power line inspection. Classical linear controllers offer acceptable performance but lack robustness under disturbances, unmodeled dynamics, and parameter variations. Although Sliding Mode Control (SMC) provides robustness, it suffers from chattering, which can damage quadcopter actuators. 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 unmolded effects, such as aerodynamic drag and propeller-induced forces, resulting in a more realistic and accurate representation of quadcopter flight dynamics. Particle Swarm Optimization (PSO) was used to tune the sliding-surface gains of the Adaptive Super-Twisting Sliding Mode Controller (ASTSMC), Reducing attitude objective values by 4.43-27.22% and improving position accuracy by up to 10.62%. The Global Best (GBEST) value dropped from 10.49 to 7.66, reflecting a 26.97% reduction in combined tracking error and control effort. Overall, PSO-tuned gains yield faster convergence, smoother responses, and better performance than manual tuning. The proposed PSO optimized ASTSMC significantly enhances quadrotor trajectory tracking by reducing attitude errors by up to 27% and improving position accuracy by 0.61-10.62%. Simulation results show faster convergence, stronger disturbance rejection, smoother control effort, and superior transient and steady-state performance compared to both conventional PID and back stepping sliding mode control.

Keywords:

particle swarm optimization, adaptive super twisting sliding mode control, quaternion modeling, unmanned aerial vehicle

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