Proceedings Article10.1109/CCECE.2003.1226243
Closed-loop system identification using subspace-based methods
Y. Zhao,David T. Westwick +1 more
- 04 May 2003
- Vol. 3, pp 1727-1730
5
TL;DR: A new way to identify a LTI system operating in a closed-loop experiment using subspace-based methods and a simulation example is provided to show how the algorithm works.
read more
Abstract: System identification using subspace-based methods has been a hot research topic in the past few years. Subspace methods fit state space models directly from the input and output signals measured from systems. In this paper we present a new way to identify a LTI system operating in a closed-loop experiment. One of the MOESP identification methods is adapted to identify the deterministic part of system operating in closed-loop experiment with nonwhite process noise and white output noise. A state space model will be computed to provide the relationship between the input and output of the plant. A simulation example is also provided to show how the algorithm works.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Subspace-based identification for linear and nonlinear systems
Harish J. Palanthandalam-Madapusi,S.L. Lacy,Jesse B. Hoagg,Dennis S. Bernstein +3 more
- 08 Jun 2005
TL;DR: A simplified proof of the fact that the state sequence and/or the observability matrix of the dynamical system can be determined directly from input-output data is provided.
Performance Measure of Some Subspace-Based Methods for Closed-Loop System Identification
Muhammad Hilmi R. A. Aziz,Rosmiwati Mohd-Mokhtar +1 more
- 28 Sep 2010
TL;DR: In this paper, three methods have been observed, those are the ORT method, MOESP method and CCA method, which are evaluated by identifying the same experimental systems and three performance evaluation tests according to mean square errors, variance accounted for and best fit are used to verify the accuracy of the models to identify the given systems.
6
Closed-loop system identification of ankle dynamics using a subspace method with reference input as instrumental variable
Yong Zhao,Daniel Ludvig,Robert E. Kearney +2 more
- 11 Jun 2008
TL;DR: A MOESP (multivariable output-error state-space) subspace system identification method is used to estimate the dynamics of each pathway directly from measured data, and it is shown that the method produces accurate results.
A direct approach to identify closed loop Wiener systems, whose linear dynamics are open-loop unstable
Yong Zhao,David T. Westwick +1 more
- 01 Jan 2004
TL;DR: The main contribution of this paper is the design of an extended Kalman filter, which is used to estimate the states of the linear subsystem as well as the parameters of the nonlinearity.
Stochastic subspace algorithm based on the orthogonal decomposition method for closed-loop system identification
Muhammad Hilmi R. A. Aziz,Rosmiwati Mohd-Mokhtar +1 more
- 01 Dec 2010
TL;DR: In this article, a stochastic subspace identification algorithm dedicated to subspace-based closed-loop system identification is presented, which is based on the ordinary subspace technique followed by a modification in which a new proposed method based on orthogonal decomposition method is used to reconstruct the past input and past output data of the instrumental variables.
References
•Book
System Identification: Theory for the User
Lennart Ljung
- 01 Jan 1987
TL;DR: Das Buch behandelt die Systemidentifizierung in dem theoretischen Bereich, der direkte Auswirkungen auf Verstaendnis and praktische Anwendung der verschiedenen Verfahren zur IdentifIZierung hat.
N4SID: subspace algorithms for the identification of combined deterministic-stochastic systems
TL;DR: Two new N4SID algorithms to identify mixed deterministic-stochastic systems are derived and these new algorithms are compared with existing subspace algorithms in theory and in practice.
2.1K
Identification of the deterministic part of MIMO state space models given in innovations form from input-output data
TL;DR: Two algorithms to identify a linear, time-invariant, finite dimensional state space model from input-output data and a special case of the recently developed Multivariable Output-Error State Space (MOESP) class of algorithms based on instrumental variables are described.
976
A unifying theorem for three subspace system identification algorithms
Peter Van Overschee,Bart De Moor +1 more
TL;DR: It is shown that all three algorithms are special cases of one unifying theorem and that the weighting matrix, used to calculate a basis for the column space of the observability matrix is different in the three cases.
435