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Abstract

This Chapter provides an extensive evaluation and comparison of the controller designs that have been presented in this book. The comparative study is completed by executing several nonaggressive and aggressive flight maneuvers that test the derived controllers in terms of stability and tracking accuracy. The test maneuvers are produced by inertial position (or velocity) and yaw reference trajectories. The reference trajectories are specially designed in order to examine the performance of the controllers in multiple operating conditions that cover a wide portion of the flight envelope. Some of the reference trajectories are particularly aggressive investigating the physical limitations of the helicopter. The controllers were tested for the Raptor 90 SE RC helicopter, which operates in the X-Plane flight simulator environment.

Keywords

Pitch Angle Controller Design Reference Trajectory Main Rotor Forward Flight 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 47.
    T.J. Koo, S. Sastry, Output tracking control design of a helicopter model based on approximate linearization, in Proceedings of the 37th IEEE Conference on Decision and Control, vol. 4, 1998, pp. 3635–3640 Google Scholar
  2. 66.
    L. Marconi, R. Naldi, Robust full degree-of-freedom tracking control of a helicopter. Automatica 43, 1909–1920 (2007) MathSciNetzbMATHCrossRefGoogle Scholar
  3. 70.
    B. Mettler, Identification Modeling and Characteristics of Miniature Rotorcraft (Kluwer Academic Publishers, Norwell, 2003) CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringGeorgia Institute of TechnologyAtlantaUSA
  2. 2.Department of Electrical and Computer Engineering, and, Department of Computer Science, School of Engineering and Computer ScienceUniversity of DenverDenverUSA

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