Observer/Kalman Filter Identié cation for Online System Identié cation of Aircraft

The observer/Kalman e lter identie cation method is applied to the problem of online system identie cation of accurate,locallylinear,aircraftdynamicmodelsofnonlinearaircraft.Itisa time-domaintechniquethatidentie esa discreteinput ‐outputmapping from known inputand outputdata samples,withoutuserimposed a prioriassumptions about model structure or model order. The basic formulation of observer/Kalman e lter identie cation specie c to theaircraftproblem isdeveloped and implementedina nonlinear,six-degree-of-freedom simulation of an AV-8B Harrier. A similar simulation of a generic uninhabited combat aerial vehicle is also used. Numerical examples are presented, consisting of longitudinal and lateral/directional successive online identie cations at different nonperfect trim conditions, identie cation with sensor noise on multiple channels, and identie cation with discrete gusts. Accuracy of the identie ed linear system models to the nonlinear plant is quantie ed with comparison of eigenvalues, the Vinnicombe gap metric, and time history matching. Results demonstrate that the observer/Kalman e lter identie cation method is suitable for aircraft online identie cation of locally linear aircraft models and is generally insensitive to moderate intensity Gaussian white sensor noise and for light to moderate intensity discrete gusts.

[1]  J. Juang,et al.  Predictive feedback and feedforward control for systems with unknown disturbances , 1999 .

[2]  Richard W. Longman,et al.  Identification of linear multivariable systems by identification of observers with assigned real eigenvalues , 1992 .

[3]  Karthik Krishnamurthy,et al.  Intelligent systems for autonomous aircraft , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[4]  Richard W. Longman,et al.  Identification of linear systems by an asymptotically stable observer , 1992 .

[5]  Dong-Chan Lee,et al.  Improved coupled force and moment parameter estimation for aircraft , 1999 .

[6]  Jen-Kuang Huang,et al.  Integrated system identification and state estimation for control of flexible space structures , 1992 .

[7]  M. Phan,et al.  Integrated system identification and state estimation for control offlexible space structures , 1992 .

[8]  K. W. Iliff,et al.  Parameter Estimation for Flight Vehicles , 1989 .

[9]  Eugene A. Morelli,et al.  Real-Time Parameter Estimation in the Frequency Domain , 1999 .

[10]  Jer-Nan Juang.  An Eigensystem Realization Algorithm for Application to Modal Testing , 1985 .

[11]  Jer-Nan Juang,et al.  An eigensystem realization algorithm for modal parameter identification and model reduction. [control systems design for large space structures] , 1985 .

[12]  H. Friehmelt.  Quality Effects on Parameter Identification - An X-31A Study Case. , 1997 .

[13]  Petros G. Voulgaris,et al.  Parameter identification for systems with redundant actuators , 1998 .

[14]  Hans-Christoph Oelker,et al.  Aerodynamic System Identification of Nonlinear Rudder Characteristics based on Flight Test Data , 1998 .

[15]  Donald Ward,et al.  An intelligent inference engine for autonomous aerial vehicles , 1999 .

[16]  Donald Ward,et al.  An intelligent flight director for autonomous aircraft , 2000 .

[17]  Charles O’Neill.  Aerodynamic System Identification MAE 6243 Project , 2004 .

[18]  Kamesh Subbarao,et al.  Non-linear adaptive auto-pilot for uninhabited aerial combat vehicles , 1999 .

[19]  David G. Ward,et al.  Development and Flight Testing of a Parameter Identification Algorithm for Reconfigurable Control , 1998 .

[20]  Calmet Meteorological Model.  A User's Guide for the , 1999 .

[21]  John L. Junkins,et al.  Inverse dynamics approach for real-time determination of feasible aircraft reference trajectories , 1999 .

[22]  Wright-Patterson Afb,et al.  Integration of On-line System Identification and Optimization-based Control Allocation* , 1998 .

[23]  Eugene A. Morelli,et al.  Accuracy of Aerodynamic Model Parameters / Estimated from Flight Test Data , 1997 .

[24]  Jer-Nan Juang,et al.  Some experiences with the Eigensystem Realization Algorithm , 1988 .

[25]  J. E. Cooper.  On-Line Version of the Eigensystem Realization Algorithm Using Data Correlations , 1997 .

[26]  Neil E. Goodzeit,et al.  System Identification in the Presence of Completely Unknown Periodic Disturbances , 2000 .

[27]  Kamesh Subbarao,et al.  A novel trajectory tracking methodology using structured adaptive model inversion for uninhabited aerial vehicles , 2000, Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334).

[28]  Β. L. HO,et al.  Editorial: Effective construction of linear state-variable models from input/output functions , 1966 .

[29]  J. Juang,et al.  Effects of Noise on Modal Parameters Identified by the Eigensystem Realization Algorithm , 1986 .

[30]  G. Vinnicombe.  Frequency domain uncertainty and the graph topology , 1993, IEEE Trans. Autom. Control..

[31]  M. Phan,et al.  Identification of observer/Kalman filter Markov parameters: Theory and experiments , 1993 .