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<p>To tackle the challenges posed by disturbances and uncertainties in helicopters, this article introduces a neural-network-based anti-disturbance integral sliding mode control method. The proposed methodology employs feedback linearization technique to mitigate the nonlinear characteristics of the helicopter altitude and attitude system, thereby enabling the effective design of an integral sliding surface. To comprehensively handle both external disturbances and system uncertainties, the control architecture integrates a dual-observer system comprising a nonlinear disturbance observer and a neural network observer. The proposed control method enhances system robustness, demonstrating superior performance over traditional sliding mode control methods through faster response time and reduced chattering effects. The control system’s stability is conclusively demonstrated through Lyapunov stability theory. The simulation results confirm that our proposed method effectively improves system stability and robustness across diverse conditions.</p>

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Neural-Network-Based Anti-Disturbance Integral Sliding Mode Flight Control for Helicopters

  • 期刊论文

  • 发表日期：

    2025年8月11日

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  • Zhuoyang Li,
  • Yankai Li,
  • Yingmin Yi,
  • Dongping Li,
  • Ruihan He,
  • Pu Zhang

摘要

To tackle the challenges posed by disturbances and uncertainties in helicopters, this article introduces a neural-network-based anti-disturbance integral sliding mode control method. The proposed methodology employs feedback linearization technique to mitigate the nonlinear characteristics of the helicopter altitude and attitude system, thereby enabling the effective design of an integral sliding surface. To comprehensively handle both external disturbances and system uncertainties, the control architecture integrates a dual-observer system comprising a nonlinear disturbance observer and a neural network observer. The proposed control method enhances system robustness, demonstrating superior performance over traditional sliding mode control methods through faster response time and reduced chattering effects. The control system’s stability is conclusively demonstrated through Lyapunov stability theory. The simulation results confirm that our proposed method effectively improves system stability and robustness across diverse conditions.