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<p>This paper presents the design of a model-free controller using radial basis function neural networks (RBFNNs) and sliding mode (SM) estimators for a coaxial-rotor aircraft system in the presence of unknown dynamics and input saturation. The proposed control approach decomposes the overall dynamic of the aircraft system into a set of interconnected subsystems, employing a nonlinear feedback control approach. The SM estimator is introduced for each subsystem to approximate the unknown dynamic functions with the saturation error provided, while an adaptive RBFNN is incorporated to compensate the estimation error. The global asymptotical stability of the closed-loop control system is established using Lyapunov theory. Numerical simulations conducted on a coaxial-rotor system template demonstrate the robustness and effectiveness of the proposed control strategy.</p>

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Adaptive neural sliding controller for coaxial-rotor aircraft system with input saturation: a model-free control approach

  • 期刊论文

  • 发表日期：

    2025年4月30日

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  • Abdelghani Chelihi,
  • Hossam Eddine Glida,
  • Chouki Sentouh,
  • Jagat Jyoti Rath

摘要

This paper presents the design of a model-free controller using radial basis function neural networks (RBFNNs) and sliding mode (SM) estimators for a coaxial-rotor aircraft system in the presence of unknown dynamics and input saturation. The proposed control approach decomposes the overall dynamic of the aircraft system into a set of interconnected subsystems, employing a nonlinear feedback control approach. The SM estimator is introduced for each subsystem to approximate the unknown dynamic functions with the saturation error provided, while an adaptive RBFNN is incorporated to compensate the estimation error. The global asymptotical stability of the closed-loop control system is established using Lyapunov theory. Numerical simulations conducted on a coaxial-rotor system template demonstrate the robustness and effectiveness of the proposed control strategy.