Subspace identification of an aircraft linear parameter-varying flutter model

The process of system identification of an linear parameter-varying (LPV) system using subspace techniques is demonstrated by application to the widely used pitch-plunge model of an aircraft flutter simulation. The identification is done using a recently published subspace identification (SID) algorithm [1] for LPV systems. The objective is to demonstrate the ability of this method to not only identify a highly accurate model in state space form, but to also determine the state order of the system. As the identification data are gained from simulation, a comparison is given between the noiseless and the noisy case, and the effects of noise especially on the model order estimation are discussed.

[1]  Wallace E. Larimore,et al.  Canonical variate analysis in identification, filtering, and adaptive control , 1990, 29th IEEE Conference on Decision and Control.

[2]  V. Verdult Non linear system identification : a state-space approach , 2002 .

[3]  W. Larimore System Identification, Reduced-Order Filtering and Modeling via Canonical Variate Analysis , 1983, 1983 American Control Conference.

[4]  M. Verhaegen,et al.  Identifying MIMO Hammerstein systems in the context of subspace model identification methods , 1996 .

[5]  J. Maciejowski,et al.  An improved subspace identification method for bilinear systems , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

[6]  V. Verdult,et al.  Filtering and System Identification: A Least Squares Approach , 2007 .

[7]  Michael Buchholz Subspace-Identification zur Modellierung von PEM-Brennstoffzellen-Stacks , 2010 .

[8]  Si-Zhao Joe Qin,et al.  An overview of subspace identification , 2006, Comput. Chem. Eng..

[9]  Alessandro Chiuso,et al.  Consistency analysis of some closed-loop subspace identification methods , 2005, Autom..

[10]  M. Verhaegen,et al.  Identifying MIMO Wiener systems using subspace model identification methods , 1995, Proceedings of 1995 34th IEEE Conference on Decision and Control.

[11]  Vincent Verdult,et al.  Kernel methods for subspace identification of multivariable LPV and bilinear systems , 2005, Autom..

[12]  W. E. Larimore,et al.  Automated multivariable system identification and industrial applications , 1999, Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251).

[13]  Michel Verhaegen,et al.  LPV identification of an aeroelastic flutter model , 2010, 49th IEEE Conference on Decision and Control (CDC).

[14]  Richard J. Prazenica,et al.  Identifying Parameter-Dependent Volterra Kernels to Predict Aeroelastic Instabilities , 2004 .

[15]  Patrick Dewilde,et al.  Subspace model identification Part 1. The output-error state-space model identification class of algorithms , 1992 .

[16]  Muhammad R. Hajj,et al.  Higher-Order Spectral Analysis of a Nonlinear Pitch and Plunge Apparatus , 2005 .

[17]  J. W. Van Wingerden,et al.  Control of wind turbines with 'Smart' rotors : Proof of concept & LPV subspace identification , 2008 .

[18]  Michael Buchholz,et al.  Identification of a bilinear and parameter-varying model for lithium-ion batteries by subspace methods , 2013, 2013 American Control Conference.

[19]  W. Larimore,et al.  ADAPT-lpv Software for Identification of Nonlinear Parameter-Varying Systems , 2012 .

[20]  Roland Toth,et al.  Modeling and Identification of Linear Parameter-Varying Systems , 2010 .

[21]  W. Larimore Identification of nonlinear parameter-varying systems via canonical variate analysis , 2013, 2013 American Control Conference.

[22]  D. Bauer Some asymptotic theory for the estimation of linear systems using maximum likelihood methods or subspace algorithms , 1998 .

[23]  W.E. Larimore,et al.  Maximum likelihood subspace identification for linear, nonlinear, and closed-loop systems , 2005, Proceedings of the 2005, American Control Conference, 2005..

[24]  Alessandro Chiuso,et al.  On the Asymptotic Properties of Closed-Loop CCA-Type Subspace Algorithms: Equivalence Results and Role of the Future Horizon , 2010, IEEE Transactions on Automatic Control.

[25]  Wallace E. Larimore,et al.  Large Sample Efficiency for Adaptx Subspace System Identification with Unknown Feedback , 2004 .

[26]  Michel Verhaegen,et al.  Subspace identification of Bilinear and LPV systems for open- and closed-loop data , 2009, Autom..

[27]  Bart De Moor,et al.  Subspace identification of bilinear systems subject to white inputs , 1999, IEEE Trans. Autom. Control..

[28]  H. Akaike,et al.  Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .

[29]  Michael Buchholz,et al.  Recursive subspace identification of linear parameter-varying systems , 2012, 2012 American Control Conference (ACC).

[30]  Bart De Moor,et al.  Numerical algorithms for state space subspace system identification , 1993 .

[31]  J. Baillieul,et al.  Identification and filtering of nonlinear systems using canonical variate analysis , 1990, 29th IEEE Conference on Decision and Control.

[32]  Tohru Katayama,et al.  Subspace Methods for System Identification , 2005 .

[33]  Alessandro Chiuso,et al.  On the relation between CCA and predictor-based subspace identification. , 2005, CDC 2005.

[34]  Lawton H. Lee,et al.  Identification of Linear Parameter-Varying Systems Using Nonlinear Programming , 1999 .

[35]  Wallace E. Larimore,et al.  Optimal Reduced Rank Modeling, Prediction, Monitoring and Control using Canonical Variate Analysis , 1997 .

[36]  Michel Verhaegen,et al.  Subspace identification of multivariable linear parameter-varying systems , 2002, Autom..