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Nonlinear Robust Tracking Control of a Quadrotor UAV on SE(3) (1109.4457v1)

Published 21 Sep 2011 in math.OC and cs.SY

Abstract: This paper provides nonlinear tracking control systems for a quadrotor unmanned aerial vehicle (UAV) that are robust to bounded uncertainties. A mathematical model of a quadrotor UAV is defined on the special Euclidean group, and nonlinear output-tracking controllers are developed to follow (1) an attitude command, and (2) a position command for the vehicle center of mass. The controlled system has the desirable properties that the tracking errors are uniformly ultimately bounded, and the size of the ultimate bound can be arbitrarily reduced by control system parameters. Numerical examples illustrating complex maneuvers are provided.

Citations (293)

Summary

  • The paper develops robust nonlinear control for quadrotor UAVs using a geometric approach on SE(3), which globally models dynamics to avoid singularities and ambiguities.
  • The control system ensures robust tracking against bounded uncertainties in dynamics, achieving uniform ultimate boundedness of errors whose size is controllable by system parameters.
  • A hybrid control architecture supports multiple flight modes without detailed reachability analysis, enabling complex maneuvers and optimizing performance for various UAV applications.

Nonlinear Robust Tracking Control of a Quadrotor UAV on SE(3)

The paper presented by Taeyoung Lee, Melvin Leok, and N. Harris McClamroch addresses the design and implementation of nonlinear robust tracking control systems for quadrotor UAVs. These control systems are developed based on a rigorous mathematical framework utilizing the special Euclidean group SE(3), ensuring robustness against bounded uncertainties in the UAV's dynamics. The research is distinguished by its approach to directly modeling the attitude and position control on SE(3) to avoid issues like singularities and ambiguities that conventional Euler angle or quaternion-based methods encounter.

Technical Contributions

  1. Mathematical Modeling and Control Design: The dynamics of the quadrotor are expressed globally on SE(3). This provides a comprehensive framework that integrates both the translational and rotational dynamics. By doing so, the authors overcome the limitations such as the singularity of Euler angles and the unwinding phenomenon in quaternions.
  2. Nonlinear Control Systems: The control systems are designed to achieve output tracking for both attitude and position commands. These incorporate controllers that guarantee the uniform ultimate boundedness of tracking errors. The size of the ultimate bound can be controlled through system parameters, allowing for optimizing performance based on operational constraints.
  3. Robustness to Uncertainties: The system considers unstructured and bounded uncertainties in both translational and rotational dynamics. This robustness is key in enabling the execution of complex UAV maneuvers in the presence of disturbances due to aerodynamic effects, evidenced even at moderate velocities.
  4. Hybrid Control Architecture: The architecture supports multiple flight modes, including attitude, position, and velocity-controlled modes, without requiring detailed reachability analysis. This is facilitated by leveraging the almost global coverage of the attraction region for each mode within the configuration space.
  5. Numerical Demonstrations: The control strategies are validated through numerical simulations illustrating complex UAV maneuvers, emphasizing the effectiveness and robustness of the proposed control methods in realistic scenarios with uncertainties and disturbances.

Implications and Future Directions

Practically, this advanced control strategy optimizes UAV performance in diverse applications, from surveillance and sensor networks to educational uses. Theoretical implications extend to improving the geometric control design on SE(3) for various robotics applications. Future research could focus on extending these concepts to cooperative control of multiple UAVs, introducing adaptive elements for better uncertainty handling, or incorporating learning-based approaches for further optimization in dynamic environments.

The work represents a significant step toward realizing efficient, robust UAV control that maintains performance integrity under real-world uncertainties. As UAV applications become more sophisticated, the need for such reliable control systems will only increase. Future developments may also explore the intersection of this geometrical control approach with emerging AI-based control algorithms, potentially leading to hybrid systems that leverage both robust geometric control and adaptive intelligence.