Approximate Input Reconstruction for Diagnosing Aircraft Control Surfaces

An approximate input reconstruction algorithm is used to reconstruct unknown inputs, which are then used for fault detection. The approximate input reconstruction algorithm is a least squares algorithm that estimates both the unknown initial state and input history. The estimated inputs are then compared to the commanded values and sensor values to assess the health of actuators and sensors. This approach is applied to the longitudinal and lateral dynamics of NASA’s Generic Transport Model. The input reconstruction algorithm can be used for systems with minimum-phase or nonminimum-phase zeros; however, minimum-phase zeros entail an additional delay in reconstructing inputs, while zeros on the unit circle yield persistent estimation errors and thus poor input reconstruction regardless of the delay.

[1]  D. Wong,et al.  Negative Selection Algorithm for Aircraft Fault Detection , 2004, ICARIS.

[2]  W. Messner,et al.  Cancellation of Discrete Time Unstable Zeros by Feedforward Control , 1994 .

[3]  Ron J. Patton,et al.  Input Observability and Input Reconstruction , 1998, Autom..

[4]  Masayoshi Tomizuka,et al.  Experimental flexible beam tip tracking control with a truncated series approximation to uncancelable inverse dynamics , 1994, IEEE Trans. Control. Syst. Technol..

[5]  D. Prattichizzo,et al.  A unified algorithmic setting for signal-decoupling compensators and unknown-input observers , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

[6]  James L. Massey,et al.  Inverses of Linear Sequential Circuits , 1968, IEEE Transactions on Computers.

[7]  Austin M. Murch.  A Flight Control System Architecture for the NASA AirSTAR Flight Test Infrastructure , 2008 .

[8]  Steven Gillijns,et al.  Kalman filtering techniques for system inversion and data assimilation , 2007 .

[9]  Jie Chen,et al.  Design of unknown input observers and robust fault detection filters , 1996 .

[10]  L.Y. Pao,et al.  The effect of nonminimum-phase zero locations on the performance of feedforward model-inverse control techniques in discrete-time systems , 2008, 2008 American Control Conference.

[11]  Jason L. Speyer,et al.  Robust fault detection filter design , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[12]  J. Massey,et al.  Invertibility of linear time-invariant dynamical systems , 1969 .

[13]  Dennis S. Bernstein,et al.  Fault detection for aircraft control surfaces using approximate input reconstruction , 2009, 2009 American Control Conference.

[14]  Janos Gertler,et al.  Fault detection and diagnosis in engineering systems , 1998 .

[15]  Thomas L. Jordan,et al.  NASA Langley's AirSTAR Testbed: A Subscale Flight Test Capability for Flight Dynamics and Control System Experiments , 2008 .

[16]  Michel Kinnaert,et al.  Diagnosis and Fault-Tolerant Control , 2004, IEEE Transactions on Automatic Control.

[17]  Domenico Prattichizzo,et al.  Convolution profiles for right inversion of multivariable non-minimum phase discrete-time systems , 2002, Autom..

[18]  Masayoshi Tomizuka,et al.  Zero Phase Error Tracking Algorithm for Digital Control , 1987 .

[19]  Roger M. Bailey,et al.  Experimental Validation: Subscale Aircraft Ground Facilities and Integrated Test Capability , 2005 .