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Background and Objectives <p>The complexity of quadrotor unmanned aerial vehicles (UAVs) in nonlinear environments poses significant challenges, including path tracking, dealing with uncertainties, disturbances, and actuator faults. This study aims to improve the robustness and tracking ability of quadrotors by developing and analyzing multiple advanced control methods and their combinations, with a special focus on adaptive feedback linearization and sliding mode control strategies.</p> Methods <p>The research develops mathematical models for quadrotor dynamics and implements four controller schemes: Standard Feedback Linearization Control (FLC), Adaptive Feedback Linearization Control (AFLC), Standard Sliding Mode Control (SMC), and Adaptive Sliding Mode Control (ASMC). Combinations of these controllers are also explored. The controllers are evaluated under simulated scenarios both with and without the presence of various disturbances and noise (white and colored noise). Key performance indicators include tracking error, stability (verified via Lyapunov functions), response time, and robustness against uncertainties.</p> Results <p>Simulation results demonstrate:&#xa0;Noise-free environments: FLC and AFLC controllers show superior trajectory tracking performance with smoother control inputs and lower angular velocities.&#xa0;Disturbed environments (white and colored noise): ASMC and SMC outperform other methods, maintaining position and attitude tracking effectively under uncertainty.&#xa0;Controller combinations: Adaptive controller combinations, particularly AFLC and ASMC, yield significant improvements in robustness and stability, effectively minimizing trajectory tracking error and unnecessary control input variations.</p> Conclusions <p>The integration of adaptive feedback linearization and adaptive sliding mode control offers a robust approach to quadrotor control, capable of addressing external disturbances and uncertainties. The proposed control schemes enhance tracking accuracy and system stability under both ideal and challenging conditions, making them suitable for real-world UAV applications.</p>

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Quadrotor Position Control Using Fuzzy Adaptive Feedback Linearization Controller and Sliding Mode Controller

  • 期刊论文

  • 发表日期：

    2025年6月3日

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  • Paul Rodrigues,
  • José R. Nuñez-Alvarez,
  • Ahmad Alkhayyat,
  • Chethan M,
  • Ashish Singh,
  • Y. Sudha Vani,
  • Ati Priya,
  • Prasad KDV,
  • Abhinav Kumar,
  • Mostafa jalalnezhad

摘要

Background and Objectives

The complexity of quadrotor unmanned aerial vehicles (UAVs) in nonlinear environments poses significant challenges, including path tracking, dealing with uncertainties, disturbances, and actuator faults. This study aims to improve the robustness and tracking ability of quadrotors by developing and analyzing multiple advanced control methods and their combinations, with a special focus on adaptive feedback linearization and sliding mode control strategies.

Methods

The research develops mathematical models for quadrotor dynamics and implements four controller schemes: Standard Feedback Linearization Control (FLC), Adaptive Feedback Linearization Control (AFLC), Standard Sliding Mode Control (SMC), and Adaptive Sliding Mode Control (ASMC). Combinations of these controllers are also explored. The controllers are evaluated under simulated scenarios both with and without the presence of various disturbances and noise (white and colored noise). Key performance indicators include tracking error, stability (verified via Lyapunov functions), response time, and robustness against uncertainties.

Results

Simulation results demonstrate: Noise-free environments: FLC and AFLC controllers show superior trajectory tracking performance with smoother control inputs and lower angular velocities. Disturbed environments (white and colored noise): ASMC and SMC outperform other methods, maintaining position and attitude tracking effectively under uncertainty. Controller combinations: Adaptive controller combinations, particularly AFLC and ASMC, yield significant improvements in robustness and stability, effectively minimizing trajectory tracking error and unnecessary control input variations.

Conclusions

The integration of adaptive feedback linearization and adaptive sliding mode control offers a robust approach to quadrotor control, capable of addressing external disturbances and uncertainties. The proposed control schemes enhance tracking accuracy and system stability under both ideal and challenging conditions, making them suitable for real-world UAV applications.