We propose an iterative method for joint state and parameter estimation using measurements on a time interval [0, T] for systems that are backward output stabilizable. Since this time interval is fixed, errors in initial state may have a big impact on the parameter estimate. We propose to use the back and forth nudging (BFN) method for estimating the system’s initial state and a Gauss–Newton step between BFN iterations for estimating the system parameters. Taking advantage of results on the optimality of the BFN method, we show that for systems with skew-adjoint generators, the initial state and parameter estimate minimizing an output error cost functional is an attractive fixed point for the proposed method. We treat both linear source estimation and bilinear parameter estimation problems.
Mots-clés : Parameter estimation, system identification, back and forth nudging, output error minimization
@article{COCV_2018__24_1_265_0, author = {Aalto, Atte}, title = {Iterative observer-based state and parameter estimation for linear systems}, journal = {ESAIM: Control, Optimisation and Calculus of Variations}, pages = {265--288}, publisher = {EDP-Sciences}, volume = {24}, number = {1}, year = {2018}, doi = {10.1051/cocv/2017005}, mrnumber = {3843185}, zbl = {1396.93114}, language = {en}, url = {http://www.numdam.org/articles/10.1051/cocv/2017005/} }
TY - JOUR AU - Aalto, Atte TI - Iterative observer-based state and parameter estimation for linear systems JO - ESAIM: Control, Optimisation and Calculus of Variations PY - 2018 SP - 265 EP - 288 VL - 24 IS - 1 PB - EDP-Sciences UR - http://www.numdam.org/articles/10.1051/cocv/2017005/ DO - 10.1051/cocv/2017005 LA - en ID - COCV_2018__24_1_265_0 ER -
%0 Journal Article %A Aalto, Atte %T Iterative observer-based state and parameter estimation for linear systems %J ESAIM: Control, Optimisation and Calculus of Variations %D 2018 %P 265-288 %V 24 %N 1 %I EDP-Sciences %U http://www.numdam.org/articles/10.1051/cocv/2017005/ %R 10.1051/cocv/2017005 %G en %F COCV_2018__24_1_265_0
Aalto, Atte. Iterative observer-based state and parameter estimation for linear systems. ESAIM: Control, Optimisation and Calculus of Variations, Tome 24 (2018) no. 1, pp. 265-288. doi : 10.1051/cocv/2017005. http://www.numdam.org/articles/10.1051/cocv/2017005/
[1] Output error minimizing back and forth nudging method for initial state recovery. Syst. Control Lett. 94 (2016) 111–117. | DOI | MR | Zbl
,[2] Solving inverse source problems using observability. Applications to the Euler–Bernoulli plate equation. SIAM J. Control Optimiz. 48 (2009) 1632–1659. | DOI | MR | Zbl
, , and ,[3] Stabilization of Elastic Systems by Collocated Feedback. Vol. 2124 of Lect. Notes Math. Springer (2015). | DOI | MR | Zbl
and ,[4] Back and forth nudging algorithm for data assimilation problems. Comptes Rendus de l’Academie des Sciences, Série I (Mathematique) 340 (2005) 873–878. | DOI | MR | Zbl
and ,[5] A nudging-based data assimilation method: the back and forth nudging (BFN) algorithm. Nonl. Processes Geophys. 15 (2008) 305–319. | DOI
and ,[6] Global Carleman estimates for waves and applications. Commun. Partial Differ. Equ. 38 (2013) 823–859. | DOI | MR | Zbl
, and ,[7] On-line parameter estimation for infinite-dimensional dynamical systems. SIAM J. Control Optimiz. 35 (1997) 678–713. | DOI | MR | Zbl
, , and ,[8] The iterated Kalman smoother as a Gauss–Newton method. SIAM J. Optimiz. 4 (1994) 626–636. | DOI | MR | Zbl
,[9] Exponential convergence of an observer based on partial field measurements for the wave equation. Math. Problems in Eng. (2012) 581053. | MR | Zbl
, , and ,[10] Robust filtering for joint state-parameter estimation in distributed mechanical systems. Discrete and Continuous Dynamical Systems, Series A 23 (2009) 65–84. | MR | Zbl
, and[11] Particle filter scheme with mutation for the estimation of time-invariant parameters in structural health monitoring applications. Structural Control and Health Monitoring 20 (2013) 1081–1095. | DOI
and ,[12] Nonlinear Least Squares for Inverse Problems. Springer (2009). | MR | Zbl
,[13] Time-varying additive perturbations of well-posed linear systems. Math. Control, Signals, and Syst. 27 (2015) 149–185. | DOI | MR | Zbl
and ,[14] The rate at which energy decays in a damped string. Commun. Partial Differ. Equ. 19 (1994) 213–243. | DOI | MR | Zbl
and ,[15] Exponential stabilization of well-posed systems by colocated feedback. SIAM J. Control Optimiz. 45 (2006) 273–297. | DOI | MR | Zbl
and ,[16] An Introduction to Infinite Dimensional Linear Systems Theory. Vol. 21 of Texts in Applied Mathematics, Springer Verlag, New York (1995). | DOI | MR | Zbl
and ,[17] Schur complements in C*-algebras. Math. Nachrichten 278 (2005) 808–814. | DOI | MR | Zbl
, and ,[18] Parameter identification for uncertain plants using H∞ methods. Automatica 31 (1995) 1227–1250. | DOI | MR | Zbl
, and ,[19] Bayesian Filtering In Nonlinear Structural Systems With Applications To Structural Health Monitoring. Graduate College Dissertations and Theses. University of Vermont (2015) 513.
,[20] Observers and initial state recovering for a class of hyperbolic systems via Lyapunov methods. Automatica 49 (2013) 2250–2260 | DOI | MR | Zbl
,[21] Recovering the observable part of the initial data of an infinite-dimensional linear system with skew-adjoint generator. Math. Control, Signals and Syst. 26 (2014) 435–462. | DOI | MR | Zbl
,[22] Reconstructing initial data using observers: error analysis of the semi-discrete and fully discrete approximations. Numer. Math. 120 (2012) 307–343. | DOI | MR | Zbl
and ,[23] Une remarque sur la stabilisation de certains systèmes du deuxième ordre en temps. Portugaliae Mathe. 46 (1989) 245–258. | MR | Zbl
,[24] Global Lipschitz stability in an inverse hyperbolic problem by interior observations. Inverse Problems 17 (2001) 717–728. | DOI | MR | Zbl
and ,[25] A time reversal based algorithm for solving initial data inverse problems. Discrete and Continuous Dynamical Systems, Series S 4 (2011) 641–652. | DOI | MR | Zbl
, and ,[26] On persistent excitation for linear systems with stochastic coefficients. SIAM J. Control Optimiz. 40 (2001) 882–897. | DOI | MR | Zbl
and[27] Locally distributed control and damping for the conservative systems. SIAM J. Control Optimiz. 35 (1997) 1574–1590. | DOI | MR | Zbl
,[28] Asymptotic behavior of the extended Kalman filter as parameter estimator for linear systems. IEEE Trans. Automatic Control 24 (1979) 36–50. | DOI | MR | Zbl
,[29] An introduction to observers. IEEE Trans. Automatic Control 16 (1971) 596–602. | DOI
,[30] Personalization of a cardiac electromechanical model using reduced order unscented Kalman filtering from regional volumes. Medical Image Analysis 17 (2013) 816–829. | DOI
, , , , , , and[31] Impact induced composite delamination: state and parameter identification via joint and dual extended Kalman filters. Comput. Methods Appl. Mech. Eng. 194 (2005) 5242–5272. | DOI | Zbl
and ,[32] Reduced-order Unscented Kalman Filtering with application to parameter identification in large-dimensional systems. ESAIM: COCV 17 (2011) 380–405. | Numdam | MR | Zbl
and ,[33] Joint state and parameter estimation for distributed mechanical systems. Comput. Methods Appl. Mech. Eng. 197 (2008) 659–677. | DOI | MR | Zbl
, and[34] Solution of Equations in Euclidian and Banach Spaces. Academic Press, New York (1973). | MR | Zbl
,[35] Semigroups of Linear Operators and Applications to Partial Differential Equations. Springer Verlag, New York (1983). | DOI | MR | Zbl
,[36] Recovering the initial state of an infinite-dimensional system using observers. Automatica 46 (2010) 1616–1625. | DOI | MR | Zbl
, and ,[37] Persistency of excitation in continuous-time systems. Syst. Control Lett. 9 (1987) 225–233. | DOI | MR | Zbl
and ,Cité par Sources :