Predefined-time fractional-order sliding-mode control for UAV attitude tracking under disturbances

Saim Ahmed; Ahmad Taher Azar; Rozaimi Ghazali; Saim Ahmed; Ahmad Taher Azar; Rozaimi Ghazali

doi:10.3934/math.20261066

AIMS Mathematics

2026, Volume 11, Issue 8: 26590-26612. doi: 10.3934/math.20261066

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Predefined-time fractional-order sliding-mode control for UAV attitude tracking under disturbances

1.
Automated Systems and Computing Lab (ASCL), Prince Sultan University, Riyadh, Saudi Arabia
2.
College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia
3.
Centre for Robotics and Industrial Automation (CeRIA), Fakulti Teknologi dan Kejuruteraan Elektrik (FTKE), Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya 76100, Durian Tunggal, Melaka, Malaysia

Received: 21 May 2026 Revised: 03 August 2026 Accepted: 05 August 2026 Published: 25 August 2026
MSC : 93B52, 93C10, 93C40, 93D09, 93D21

Unmanned aerial vehicles (UAVs) are important for various civil and industrial tasks, including package delivery, crop inspection, and disaster assessment. These applications require precise course tracking and reliable performance under adverse weather conditions, including high winds, payload variations, and modeling uncertainties. Classical robust control methods, such as sliding mode control (SMC), are widely favored for their robustness against uncertain dynamics. However, conventional SMC suffers from chattering and may exhibit relatively slow convergence. Finite-time sliding-mode control (FTSMC) algorithms improve the convergence rate; however, their settling time depends on the initial conditions. Predefined-time fractional-order sliding mode control (PFSMC) ensures state convergence within a designer-specified interval. In this paper, a comprehensive study of the PFSMC for UAV systems is presented. A dynamic model of the UAVs is presented, a control rule with predefined time guarantees is proposed, and a Lyapunov theory is used to guarantee stability. The proposed PFSMC is simulated and compared with FTSMC, fixed-time SMC (FxSMC), and fractional FxSMC (FFxSMC), demonstrating improved robustness, tracking performance, and reduced chattering. The results show that the PFSMC is an optimal control method for UAV systems when robustness and predefined-time guarantees are required.
- UAV,
- fractional-order control,
- predefined-time,
- sliding mode control,
- attitude control
Citation: Saim Ahmed, Ahmad Taher Azar, Rozaimi Ghazali. Predefined-time fractional-order sliding-mode control for UAV attitude tracking under disturbances[J]. AIMS Mathematics, 2026, 11(8): 26590-26612. doi: 10.3934/math.20261066

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Abstract

Unmanned aerial vehicles (UAVs) are important for various civil and industrial tasks, including package delivery, crop inspection, and disaster assessment. These applications require precise course tracking and reliable performance under adverse weather conditions, including high winds, payload variations, and modeling uncertainties. Classical robust control methods, such as sliding mode control (SMC), are widely favored for their robustness against uncertain dynamics. However, conventional SMC suffers from chattering and may exhibit relatively slow convergence. Finite-time sliding-mode control (FTSMC) algorithms improve the convergence rate; however, their settling time depends on the initial conditions. Predefined-time fractional-order sliding mode control (PFSMC) ensures state convergence within a designer-specified interval. In this paper, a comprehensive study of the PFSMC for UAV systems is presented. A dynamic model of the UAVs is presented, a control rule with predefined time guarantees is proposed, and a Lyapunov theory is used to guarantee stability. The proposed PFSMC is simulated and compared with FTSMC, fixed-time SMC (FxSMC), and fractional FxSMC (FFxSMC), demonstrating improved robustness, tracking performance, and reduced chattering. The results show that the PFSMC is an optimal control method for UAV systems when robustness and predefined-time guarantees are required.

References

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