Embedded estimation of fault parameters in an unmanned aerial vehicle

In this paper, we present a model-based approach for estimating fault conditions in an aircraft. We formulate fault estimation as a convex optimization problem, where estimates are obtained by solving a constrained quadratic program (QP). A moving horizon framework is used to enable recursive implementation of the constrained QP of fixed size. The estimation scheme takes into account a priori known monotonicity constraints on the faults. Monotonicity implies that the fault conditions can only deteriorate with time. We validate the proposed estimation scheme on a detailed nonlinear simulation model of the Aerosonde unmanned aerial vehicle (UAV) in the presence of winds and turbulence. An excellent performance of the developed approach is demonstrated.

[1]  Rolf Isermann,et al.  Process Fault Detection Based on Modeling and Estimation Methods , 1982 .

[2]  Rolf Isermann,et al.  Process fault detection based on modeling and estimation methods - A survey , 1984, Autom..

[3]  J.J. Gertler,et al.  Survey of model-based failure detection and isolation in complex plants , 1988, IEEE Control Systems Magazine.

[4]  Paul M. Frank,et al.  Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: A survey and some new results , 1990, Autom..

[5]  Wright-Patterson Afb,et al.  Regularization Techniques for Real-Time Identification of Aircraft Parameters , 1997 .

[6]  Jie Chen,et al.  Robust Model-Based Fault Diagnosis for Dynamic Systems , 1998, The International Series on Asian Studies in Computer and Information Science.

[7]  Alberto Isidori,et al.  A Geometric Approach to Nonlinear Fault Detection and Isolation , 2000 .

[8]  Alberto Isidori,et al.  A geometric approach to nonlinear fault detection and isolation , 2000, IEEE Trans. Autom. Control..

[9]  S. Glavaski,et al.  Active aircraft fault detection and isolation , 2001, 2001 IEEE Autotestcon Proceedings. IEEE Systems Readiness Technology Conference. (Cat. No.01CH37237).

[10]  Marios M. Polycarpou,et al.  A robust detection and isolation scheme for abrupt and incipient faults in nonlinear systems , 2002, IEEE Trans. Autom. Control..

[11]  Mark Campbell,et al.  Estimation architecture for future autonomous vehicles , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

[12]  David Q. Mayne,et al.  Constrained state estimation for nonlinear discrete-time systems: stability and moving horizon approximations , 2003, IEEE Trans. Autom. Control..

[13]  Stephen P. Boyd,et al.  Moving horizon filter for monotonic trends , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[14]  Donald L. Simon,et al.  Evaluation of an Enhanced Bank of Kalman Filters for In-Flight Aircraft Engine Sensor Fault Diagnostics , 2005 .

[15]  D. Gorinevsky,et al.  Model predictive estimation of evolving faults , 2006, 2006 American Control Conference.