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

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

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...

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Bibliographic Details
Main Authors: Zhong Wei Liu, Si Bo Huang, Tian Yu Zhang, He Wang
Format: Article
Language:English
Published: Elsevier 2025-11-01
Series:Ain Shams Engineering Journal
Subjects:
Particle Swarm Optimization
Sliding mode controller
CEC2022
VTOL UAV
Parameter tuning
Online Access:http://www.sciencedirect.com/science/article/pii/S2090447925004356
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Summary: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.
ISSN:2090-4479

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