Immersion and invariance adaptive controller and mixer for coaxial tilt-rotor UAV

Journal Article (2025)
Author(s)

Longlong Chen (Dalian University of Technology)

Yanmei Jia (Dalian Minzu University)

S. Sun (TU Delft - Learning & Autonomous Control)

Zongyang Lv (University of Alberta)

Yuhu Wu (Dalian University of Technology)

Research Group
Learning & Autonomous Control
DOI related publication
https://doi.org/10.1016/j.isatra.2025.07.015
More Info
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Publication Year
2025
Language
English
Research Group
Learning & Autonomous Control
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/publishing/publisher-deals Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.@en
Volume number
166
Pages (from-to)
353-363
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Abstract

This study presents a motion control system for a coaxial tilt-rotor (CTR) unmanned aerial vehicle (UAV) equipped with two CTR modules and a tail rotor. The existing adaptive control strategies for CTRUAVs fail to guarantee the theoretical convergence of estimated parameters to their true values. Additionally, the existing mixer requires frequent and inefficient adjustments of the tilt angles for motion control. To address these issues, this work proposes a control strategy that integrates a robust integral of the sign of the error (RISE)-based immersion and invariance (I&I) adaptive controller with segmented gains and an improved mixer. The RISE-based adaptive controller is theoretically capable of estimating and compensating for external disturbance torques and forces with bounded derivatives. Furthermore, a model of the CTR module that accounts for differences between the upper and lower rotors is introduced, and the proposed mixer is designed to realize efficient control at varying tilt angles of the CTR modules. Experimental results demonstrate that the proposed control scheme significantly improves stability, transient response speed, disturbance rejection performance, and parameter estimation accuracy compared to existing control strategies for the CTRUAV.

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