<p>This paper investigates the adaptive finite-time trajectory tracking problem with full state constraints for quadrotor unmanned aerial vehicles (UAVs) in the presence of external disturbances. During the controller construction process, the non-asymmetric time-varying barrier functions and finite-time theory are employed to ensure that all the states of the quadrotor system and the trajectory tracking errors converge to the predefined boundaries within a finite time. Meanwhile, disturbance observers are constructed for the composite disturbances in the model, which are composed of external disturbances and fuzzy approximation errors, to achieve accurate observation of the disturbance signals. In addition, the command filtering technology and fuzzy logic systems are incorporated into the control framework. The former avoids the complex computational issues caused by differentiating the virtual control laws, while the latter effectively approximates the unknown nonlinear terms in the quadrotor model. Finally, simulation results validate the effectiveness of the proposed strategy.</p>

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Command-Filtered Adaptive Finite-Time Control of Trajectory Tracking for Quadrotor UAV with Full State Constraints

  • Mingchao Liu,
  • Ming Chen,
  • Yulin Gai,
  • Xiaoxuan Jiao

摘要

This paper investigates the adaptive finite-time trajectory tracking problem with full state constraints for quadrotor unmanned aerial vehicles (UAVs) in the presence of external disturbances. During the controller construction process, the non-asymmetric time-varying barrier functions and finite-time theory are employed to ensure that all the states of the quadrotor system and the trajectory tracking errors converge to the predefined boundaries within a finite time. Meanwhile, disturbance observers are constructed for the composite disturbances in the model, which are composed of external disturbances and fuzzy approximation errors, to achieve accurate observation of the disturbance signals. In addition, the command filtering technology and fuzzy logic systems are incorporated into the control framework. The former avoids the complex computational issues caused by differentiating the virtual control laws, while the latter effectively approximates the unknown nonlinear terms in the quadrotor model. Finally, simulation results validate the effectiveness of the proposed strategy.