Virtual Sensor for Failure Detection, Identification and Recovery in the Transition Phase of a Morphing Aircraft

The Helicopter Adaptive Aircraft (HADA) is a morphing aircraft which is able to take-off as a helicopter and, when in forward flight, unfold the wings that are hidden under the fuselage, and transfer the power from the main rotor to a propeller, thus morphing from a helicopter to an airplane. In this process, the reliable folding and unfolding of the wings is critical, since a failure may determine the ability to perform a mission, and may even be catastrophic. This paper proposes a virtual sensor based Fault Detection, Identification and Recovery (FDIR) system to increase the reliability of the HADA aircraft. The virtual sensor is able to capture the nonlinear interaction between the folding/unfolding wings aerodynamics and the HADA airframe using the navigation sensor measurements. The proposed FDIR system has been validated using a simulation model of the HADA aircraft, which includes real phenomena as sensor noise and sampling characteristics and turbulence and wind perturbations.

[1]  Jie Chen,et al.  Observer-based fault detection and isolation: robustness and applications , 1997 .

[2]  Pedro J. Boschetti,et al.  Stability and Flying Qualities of an Unmanned Airplane Using the Vortex-Lattice Method , 2009 .

[3]  Biao Huang,et al.  System Identification , 2000, Control Theory for Physicists.

[4]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[5]  B. Saha,et al.  A Fault Detection and Reconfigurable Control Architecture for Unmanned Aerial Vehicles , 2005, 2005 IEEE Aerospace Conference.

[6]  Frederic M. Hoblit,et al.  Gust Loads on Aircraft: Concepts and Applications , 1988 .

[7]  Domenico Accardo,et al.  Real-Time Simulation and Data Fusion of Navigation Sensors for Autonomous Aerial Vehicles , 2007 .

[8]  M. Oosterom,et al.  Virtual sensor for fault detection and isolation in flight control systems - fuzzy modeling approach , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

[9]  G. Heredia,et al.  Sensor fault detection in small autonomous helicopters using observer/Kalman filter identification , 2009, 2009 IEEE International Conference on Mechatronics.

[10]  Mario Innocenti,et al.  Kalman filters and neural-network schemes for sensor validation in flight control systems , 1998, IEEE Trans. Control. Syst. Technol..

[11]  Guillermo Heredia,et al.  Sensor and actuator fault detection in small autonomous helicopters , 2008 .

[12]  Andrzej Tomczyk.  Simple virtual attitude sensors for general aviation aircraft , 2006 .

[13]  G. N. Fouskitakis,et al.  Aircraft angle-of-attack virtual sensor design via a functional pooling narx methodology , 2003, 2003 European Control Conference (ECC).

[14]  Mario Innocenti,et al.  Helicopter Flight Dynamics: The Theory and Application of Flying Qualities and Simulation Modeling , 1999 .

[15]  Timothy A. Lewis,et al.  Flight Data Analysis and Simulation of Wind Effects During Aerial Refueling , 2008 .

[16]  Daniel J. Inman,et al.  Modeling and Flight Control of Large-Scale Morphing Aircraft , 2007 .

[17]  Hyeonsoo Yeo,et al.  Performance and Design Investigation of Heavy Lift Tilt-Rotor with Aerodynamic Interference Effects , 2007 .

[18]  Fredrik Gustafsson,et al.  Adaptive filtering and change detection , 2000 .

[19]  Aníbal Ollero,et al.  Control and perception techniques for aerial robotics , 2003, Annu. Rev. Control..

[20]  M. V. Cook.  Flight Dynamics Principles , 1997 .

[21]  Hyeonsoo Yeo,et al.  Optimum Design of a Compound Helicopter , 2009 .