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STABILITY ANALYSIS OF A FRACTIONAL ORDERED QUADROTOR UNMANNED AERIAL VEHICLE CHAOTIC SYSTEM

LAMIA LOUDAHI

https://orcid.org/0009-0001-6521-2309

School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, Zhejiang, P. R. China

Zhejiang Institute of Photoelectronics, Jinhua, Zhejiang 321004, China

E-mail Address: lamialoudahi@zjnu.edu.cn

Corresponding author.

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JING YUAN

https://orcid.org/0000-0002-3548-5875

School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, Zhejiang, P. R. China

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, and

FATENE DIOUBI

https://orcid.org/0000-0002-8002-5114

School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, Zhejiang, P. R. China

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https://doi.org/10.1142/S0218348X25500057Cited by:0 (Source: Crossref)

Abstract

A Caputo fractional-order derivative is used in a Quadrotor Unmanned Aerial Vehicle (QUAV) chaotic system to transform it into fractional ordered QUAV (FoQUAV) chaotic system for the first time. Therefore, this study explores three control (adaptive, active, and passive) strategies applied on the FoQUAV system. Further, utilizing Lyapunov theory, we established global stability around the equilibrium point and conducted numerical simulations for validation and comparison. In this work, adaptive control technique is used by considering $a_{f}$ $a_{f}$ , $a_{g}$ $a_{g}$ , and $a_{h}$ $a_{h}$ as unknown parameters and are derived using updated law. Similarly, active control technique is derived using two sub-controllers including ${\hat{μ}}_{i} = A_{i} + N_{i}$ ${\hat{μ}}_{i} = A_{i} + N_{i}$ with $A_{i}$ $A_{i}$ ; linear and $N_{i}$ $N_{i}$ ; nonlinear controllers, respectively. Finally, a feedback linearization-based, passive controller is used, which has predefined control input and converges to a stable equilibria. Moreover, this work also revealed, during performing numerical simulations, that the open loop system stabilizes at times $t \approx 4$ $t \approx 4$ s, $t \approx 60$ $t \approx 60$ s, and $t \approx 90$ $t \approx 90$ s, respectively, for adaptive, active, and passive controllers. Finally, the passive controller face several challenges during stabilizing the yaw of a considered system and overall is proved that adaptive controller perform best with respect to time consumption and quick performance in comparison.

Keywords:

References

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