Dual-loop control and state prediction analysis of QUAV trajectory tracking based on biological swarm intelligent optimization algorithm

Quadrotor unmanned aerial vehicles (QUAVs) have attracted significant research focus due to their outstanding Vertical Take-Off and Landing (VTOL) capabilities. This research addresses the challenge of maintaining precise trajectory tracking in QUAV systems when faced with external disturbances by i...

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Bibliographic Details
Published in:Scientific reports Vol. 14; no. 1; pp. 19091 - 25
Main Authors: Zou, Zuoming, Yang, Shuming, Zhao, Liang
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 17.08.2024
Nature Publishing Group
Nature Portfolio
Subjects:
639/166
639/705
Control systems
Data-driven
Deep learning
Humanities and Social Sciences
Long short-term memory
multidisciplinary
Particle swarm optimization
Quadrotor unmanned aerial vehicles
Science
Science (multidisciplinary)
Sliding mode control
Systems stability
Vertical take-off and landing
Long short-term memory
Data-driven
Vertical take-off and landing
Particle swarm optimization
Quadrotor unmanned aerial vehicles
Sliding mode control
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary:Quadrotor unmanned aerial vehicles (QUAVs) have attracted significant research focus due to their outstanding Vertical Take-Off and Landing (VTOL) capabilities. This research addresses the challenge of maintaining precise trajectory tracking in QUAV systems when faced with external disturbances by introducing a robust, two-tier control system based on sliding mode technology. For position control, this approach utilizes a virtual sliding mode control signal to enhance tracking precision and includes adaptive mechanisms to adjust for changes in mass and external disruptions. In controlling the attitude subsystem, the method employs a sliding mode control framework that secures system stability and compliance with intermediate commands, eliminating the reliance on precise models of the inertia matrix. Furthermore, this study incorporates a deep learning approach that combines Particle Swarm Optimization (PSO) with the Long Short-Term Memory (LSTM) network to foresee and mitigate trajectory tracking errors, thereby significantly enhancing the reliability and safety of mission operations. The robustness and effectiveness of this innovative control strategy are validated through comprehensive numerical simulations.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-69911-5