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In this paper the main goal is to compare the instrumental variables and the least squares methods applied to parameter estimation in continuous-time systems, avoiding any preliminary discretization of the process, and to analyse which method is more suitable for estimation in continuous-time under stochastic perturbations. A numerical example illustrates the effectiveness of the algorithms.
Escobar, Jesica 1 ; Enqvist, Martin 2
@article{COCV_2017__23_2_427_0, author = {Escobar, Jesica and Enqvist, Martin}, title = {Instrumental variables and {LSM} in continuous-time parameter estimation}, journal = {ESAIM: Control, Optimisation and Calculus of Variations}, pages = {427--442}, publisher = {EDP-Sciences}, volume = {23}, number = {2}, year = {2017}, doi = {10.1051/cocv/2015052}, mrnumber = {3608087}, zbl = {1358.93159}, language = {en}, url = {http://geodesic.mathdoc.fr/articles/10.1051/cocv/2015052/} }
TY - JOUR AU - Escobar, Jesica AU - Enqvist, Martin TI - Instrumental variables and LSM in continuous-time parameter estimation JO - ESAIM: Control, Optimisation and Calculus of Variations PY - 2017 SP - 427 EP - 442 VL - 23 IS - 2 PB - EDP-Sciences UR - http://geodesic.mathdoc.fr/articles/10.1051/cocv/2015052/ DO - 10.1051/cocv/2015052 LA - en ID - COCV_2017__23_2_427_0 ER -
%0 Journal Article %A Escobar, Jesica %A Enqvist, Martin %T Instrumental variables and LSM in continuous-time parameter estimation %J ESAIM: Control, Optimisation and Calculus of Variations %D 2017 %P 427-442 %V 23 %N 2 %I EDP-Sciences %U http://geodesic.mathdoc.fr/articles/10.1051/cocv/2015052/ %R 10.1051/cocv/2015052 %G en %F COCV_2017__23_2_427_0
Escobar, Jesica; Enqvist, Martin. Instrumental variables and LSM in continuous-time parameter estimation. ESAIM: Control, Optimisation and Calculus of Variations, Tome 23 (2017) no. 2, pp. 427-442. doi : 10.1051/cocv/2015052. http://geodesic.mathdoc.fr/articles/10.1051/cocv/2015052/
R. Bowden and A. Darrel, Instrumental Variables. Cambridge University Press (1984). | Zbl
Differential neuro-fuzzy controller for uncertain nonlinear systems. IEEE Trans. Fuzzy Systems 21 (2013) 369–384. | DOI
,Second-Order Sliding-Mode Observers for Mechanical Systems. IEEE Trans. Autom. Control 50 (2005) 1785–1789. | MR | Zbl | DOI
, and ,M.H.A. Davis, Linear Estimation and Stochastic Control. Chapman and Hall, London (1977). | Zbl | MR
C. Edwards, S. Spurgeon, Sliding Mode Control. Taylor and Francis, London (1998).
Time-varying matrix estimation in stochastic continuous-time models under colored noise using LSM with forgetting factor. Int. J. Syst. Sci. 42 (2011) 2009–2020. | Zbl | MR | DOI
and ,Robust Continuous-Time Matrix Estimation under Dependent Noise Perturbations: Sliding Modes Filtering and LSM with Forgetting. Circuits Syst. Signal Process. 28 (2009) 257–282. | Zbl | MR | DOI
and ,T. Gard, Introduction to Stochastic Differential Equations. Marcel Dekker, New York (1988). | Zbl | MR
Instrumental variable methods for continuous-time identification in closed-loop. Proc. Amer. Control Conf. 3 (2004) 2846–2851.
, and ,P. Kumar and P. Varaiya, Stochastic Systems: Estimation, Identification and Adaptive Control. Prentice Hall, Englewood Cliffs, NJ (1986). | Zbl
J.-N. Juan, Applied System Identification. Prentice Hall, Englewood Cliffs, New Jersey (1994). | Zbl
Convergence analysis of refined instrumental variable method for continuous-time system identification. IET Control Theory Appl. 5 (2011) 868–877. | MR | DOI
, and ,L. Ljung, System Identification: Theory for the User. Prentice Hall, Upper Saddle River, NJ (1999). | Zbl
High-Accuracy Instrumental Variable Identification of Continuous-Time Autoregressive Processes From Irregularly Sampled Noisy Data. IEEE Trans. Signal Process. 56 (2008) 4087–4091. | Zbl | MR | DOI
,R. Pintelon and J. Schoukens, System Identification, A Frequency Domain Approach. IEEE Press (2001).
Sliding modes time varying matrix identification for stochastic systems. Int. J. Syst. Sci. 38 (2007) 847–859. | Zbl | MR | DOI
, and ,Robust observation and Identification of nDOF Lagrangian systems. Int. J. Robust Nonlin. Control 17 (2007) 842–861. | Zbl | MR | DOI
, and ,V. Utkin, Sliding Modes Control and their Applications to Variable Structure Systems. MIR (1978).
V. Utkin, Sliding Modes in Control and Optimization. Springer-Verlag, Berlin, Heidelberg (1992). | Zbl | MR
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