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<p>This paper presents a distributed- centralized control strategy for the formation tracking of small unmanned helicopters subject to model uncertainties and external disturbances. The proposed framework addresses the highly coupled nonlinear dynamics by decomposing the system into position and attitude subsystems. In the position subsystem, a topological sliding mode controller uses inter-helicopter communication to ensure accurate formation maintenance. The attitude subsystem then utilizes a barrier function-based controller to adjust the attitude, constraining the yaw angle within predefined bounds, and control the tracking error to a small range around the equilibrium point. To enhance robustness against in-flight disturbances, an improved learning observer is integrated into the framework to provide real-time estimation and compensation for composite uncertainties. The closed-loop stability of the entire system is rigorously proven using the Lyapunov stability framework. Numerical simulations validate the effectiveness and robustness of the proposed scheme, demonstrating its capability to ensure accurate and stable helicopter formation flight in uncertain environments.</p>

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Learning observer-based distributed-centralized formation control for unmanned helicopters with uncertainties and disturbances

  • 期刊论文

  • 发表日期：

    2025年10月2日

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  • Baodi Guo,
  • Teng Cao,
  • Shaohua Yang,
  • Yongyuan Xu

摘要

This paper presents a distributed- centralized control strategy for the formation tracking of small unmanned helicopters subject to model uncertainties and external disturbances. The proposed framework addresses the highly coupled nonlinear dynamics by decomposing the system into position and attitude subsystems. In the position subsystem, a topological sliding mode controller uses inter-helicopter communication to ensure accurate formation maintenance. The attitude subsystem then utilizes a barrier function-based controller to adjust the attitude, constraining the yaw angle within predefined bounds, and control the tracking error to a small range around the equilibrium point. To enhance robustness against in-flight disturbances, an improved learning observer is integrated into the framework to provide real-time estimation and compensation for composite uncertainties. The closed-loop stability of the entire system is rigorously proven using the Lyapunov stability framework. Numerical simulations validate the effectiveness and robustness of the proposed scheme, demonstrating its capability to ensure accurate and stable helicopter formation flight in uncertain environments.