Work

Hybrid enhanced particle swarm optimization for VTOL UAV sliding mode controller: A methodological

Year: 2025

Type: article

Abstract: This paper introduces an improved Sliding Mode Controller (SMC) using Hybrid Enhanced Particle Swarm Optimization (HEPSO) for parameter tuning, optimizing c1, c2, η1, and η2. HEPSO integrates adaptive inertia weights (AIW), unified factor enhancement (UFE), and global optimal particle training (GOPT), enhancing its performance. Validated against CEC2022 benchmark functions, HEPSO excels in convergence and precision over other PSO variants. Its practicality was tested in VTOL UAV simulations, outperforming PSO-SMC, IPSO-SMC, and UPS-SMC, proving its effectiveness in UAV control systems. This study upgrades SMC performance and offers a robust control strategy for UAVs.

Source: Ain Shams Engineering Journal

DOI: 10.1016/j.asej.2025.103694

Authors: Zhong Wei Liu, Si Bo Huang, Tian Yu Zhang, He Wang

Institution: University of Science and Technology Liaoning

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,200

Funder: Department of Education of Liaoning Province