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<p>Hexacopter control is a challenging task due to nonlinear dynamics, coupling effects among motion channels, parametric uncertainties, and external disturbances. Although Sliding Mode Control (SMC) is widely used for such systems because of its robustness and stability, its practical performance is often limited by chattering, difficulty in parameter tuning, and reduced adaptability under varying operating conditions. To overcome these limitations, this paper proposes an intelligent hybrid control strategy based on the integration of an Adaptive Neuro-Fuzzy Inference System (ANFIS) with Sliding Mode Control (SMC) for hexacopter stabilization and trajectory trackin g. The main novelty of this work lies in the development of a hybrid ANFIS-SMC controller in which ANFIS is employed to adaptively tune the sliding mode control parameters in real time. This structure preserves the robustness of classical SMC while significantly enhancing tracking accuracy, transient response, and control smoothness. By incorporating the learning capability of ANFIS into the SMC framework, the proposed controller becomes more capable of handling the varying dynamic behavior of the hexacopter and reducing the limitations associated with conventional controllers. To evaluate the effectiveness of the proposed method, numerical simulations were carried out in MATLAB. The results demonstrated that the ANFIS-SMC controller outperformed the conventional SMC approach in all control channels, including roll, pitch, yaw, and translational motions xxx, yyy, and zzz. In particular, the settling time was reduced by up to 37%, the overshoot decreased by approximately 41%, and the control input chattering amplitude was reduced by more than 55%. These quantitative improvements confirm that the proposed controller not only provides faster and more accurate tracking performance but also produces smoother control signals, making it more practical for real-world implementation. Overall, the findings indicate that the ANFIS-SMC controller is an effective, intelligent, and robust solution for hexacopter control in nonlinear and uncertain environments.</p>

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Novel nonlinear control algorithms for hexacopters to overcome control challenges with nonlinear systems in the presence of uncertainty

  • 期刊论文

  • 发表日期：

    2026年7月14日

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  • Shelesh Krishna Saraswat,
  • Basem Abu Zneid,
  • B. Spoorthi,
  • Tanmoy Prida,
  • S. Radhika,
  • Gaganjot Kaur,
  • Yashwant Singh Bisht,
  • Abhayveer Singh

摘要

Hexacopter control is a challenging task due to nonlinear dynamics, coupling effects among motion channels, parametric uncertainties, and external disturbances. Although Sliding Mode Control (SMC) is widely used for such systems because of its robustness and stability, its practical performance is often limited by chattering, difficulty in parameter tuning, and reduced adaptability under varying operating conditions. To overcome these limitations, this paper proposes an intelligent hybrid control strategy based on the integration of an Adaptive Neuro-Fuzzy Inference System (ANFIS) with Sliding Mode Control (SMC) for hexacopter stabilization and trajectory trackin g. The main novelty of this work lies in the development of a hybrid ANFIS-SMC controller in which ANFIS is employed to adaptively tune the sliding mode control parameters in real time. This structure preserves the robustness of classical SMC while significantly enhancing tracking accuracy, transient response, and control smoothness. By incorporating the learning capability of ANFIS into the SMC framework, the proposed controller becomes more capable of handling the varying dynamic behavior of the hexacopter and reducing the limitations associated with conventional controllers. To evaluate the effectiveness of the proposed method, numerical simulations were carried out in MATLAB. The results demonstrated that the ANFIS-SMC controller outperformed the conventional SMC approach in all control channels, including roll, pitch, yaw, and translational motions xxx, yyy, and zzz. In particular, the settling time was reduced by up to 37%, the overshoot decreased by approximately 41%, and the control input chattering amplitude was reduced by more than 55%. These quantitative improvements confirm that the proposed controller not only provides faster and more accurate tracking performance but also produces smoother control signals, making it more practical for real-world implementation. Overall, the findings indicate that the ANFIS-SMC controller is an effective, intelligent, and robust solution for hexacopter control in nonlinear and uncertain environments.