Hexarotor Neuro Fuzzy Nonlinear Control

Document Type : Research Paper

Authors

Faculty of Mechanical Engineering, University of Tabriz, East Azerbaijan, Iran

Abstract

Sliding mode control offers several advantages over other control and observer methods when dealing with nonlinear systems, particularly in terms of stability. However, control challenges, such as uncertainties, can impact the overall system performance. In this study, new approaches have been proposed to address these issues by utilizing fuzzy neural control. This article introduces novel nonlinear control algorithms to tackle control challenges that arise with nonlinear systems in the presence of uncertainty.Hex rotors serve as excellent examples of underactuated systems, where sliding mode control demonstrates a more stable performance compared to other controllers in the presence of disturbances and uncertainties. Nonetheless, as uncertainties increase, the controller's performance diminishes. To mitigate this, an adaptive fuzzy neural network is employed to determine the control coefficients for the sliding mode controller, thus improving the system's performance in the presence of uncertainty and enhancing the system's accuracy in target tracking.This research contributes to the field of nonlinear control, offering innovative solutions to the challenges posed by uncertainty in the context of nonlinear systems, with Hex rotors serving as a compelling case study.

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References

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Journal of Modeling in Engineering
Volume 23, Special Issue 81
Celebrating the 50th Anniversary of Semnan University- In Progress
July 2025
Pages 123-137

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History

  • Receive Date: 30 November 2023
  • Revise Date: 23 September 2024
  • Accept Date: 02 October 2024

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