Fractional-order adaptive sliding mode containment control of multi-drone systems with unknown time-varying payloads and input constraints

Hanen Louati; Mohammed M. A. Almazah; Hanen Louati; Mohammed M. A. Almazah

doi:10.3934/math.20261159

AIMS Mathematics

2026, Volume 11, Issue 9: 29145-29178. doi: 10.3934/math.20261159

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Research article

Fractional-order adaptive sliding mode containment control of multi-drone systems with unknown time-varying payloads and input constraints

Hanen Louati ^{1
,},
Mohammed M. A. Almazah ^{2
,
,}

1.
Mathematics Department, Faculty of Science, Northern Border University, Arar, KSA, Saudi Arabia; Email: Hanen.Louati@nbu.edu.sa
2.
Department of Mathematics, College of Sciences, King Khalid University, Abha, 61421, Saudi Arabia

Received: 24 June 2026 Revised: 05 August 2026 Accepted: 27 August 2026 Published: 10 September 2026
MSC : 26A33, 34K37

A resilient containment control scheme was developed in this work for a category of fractional-order multi-drone networks, specifically managing the joint effects of mass changes, bounded control inputs, communication lags, component faults, and external disturbances. Unlike conventional unmanned aerial vehicle (UAV) control approaches that assume fixed or known payload parameters, the proposed framework explicitly considers unknown and time-varying payload mass, which significantly affects the dynamic behavior and stability of aerial vehicles during cooperative missions. A novel adaptive containment control protocol was developed by integrating fractional-order sliding mode control with an online payload mass adaptation mechanism, enabling each follower UAV to accurately estimate payload variations in real time. The containment objective was achieved with respect to multiple dynamic leaders under a directed communication topology. Rigorous Lyapunov stability analysis based on fractional-order Mittag–Leffler theory was provided to prove the boundedness of all closed-loop signals and the convergence of containment errors, while maintaining stability despite control input limitations and physical mechanism failures. Furthermore, the proposed control scheme ensures robustness against communication delays and matched disturbances. Extensive numerical simulations involving a 12-UAV formation scenario exhibited the effectiveness and supremacy of the proposed payload-adaptive strategy compared with non-adaptive methods, highlighting significant improvements in containment accuracy, control effort, and system robustness.
- fractional-order systems,
- multi-UAV systems,
- containment control,
- payload mass adaptation,
- fault-tolerant control,
- input saturation,
- communication delays,
- sliding mode control
Citation: Hanen Louati, Mohammed M. A. Almazah. Fractional-order adaptive sliding mode containment control of multi-drone systems with unknown time-varying payloads and input constraints[J]. AIMS Mathematics, 2026, 11(9): 29145-29178. doi: 10.3934/math.20261159

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Abstract

A resilient containment control scheme was developed in this work for a category of fractional-order multi-drone networks, specifically managing the joint effects of mass changes, bounded control inputs, communication lags, component faults, and external disturbances. Unlike conventional unmanned aerial vehicle (UAV) control approaches that assume fixed or known payload parameters, the proposed framework explicitly considers unknown and time-varying payload mass, which significantly affects the dynamic behavior and stability of aerial vehicles during cooperative missions. A novel adaptive containment control protocol was developed by integrating fractional-order sliding mode control with an online payload mass adaptation mechanism, enabling each follower UAV to accurately estimate payload variations in real time. The containment objective was achieved with respect to multiple dynamic leaders under a directed communication topology. Rigorous Lyapunov stability analysis based on fractional-order Mittag–Leffler theory was provided to prove the boundedness of all closed-loop signals and the convergence of containment errors, while maintaining stability despite control input limitations and physical mechanism failures. Furthermore, the proposed control scheme ensures robustness against communication delays and matched disturbances. Extensive numerical simulations involving a 12-UAV formation scenario exhibited the effectiveness and supremacy of the proposed payload-adaptive strategy compared with non-adaptive methods, highlighting significant improvements in containment accuracy, control effort, and system robustness.

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