Results 251 to 260 of about 93,147 (298)

Unbiased bilinear subspace system identification methods

2001 European Control Conference (ECC), 2001
Several subspace algorithms for the identification of bilinear systems have been proposed recently. A key practical problem with all of these is the very large size of the data-based matrices which must be constructed in order to ‘linearise’ the problem and allow parameter estimation essentially by regression.
Huixin Chen, Jan Maciejowski, Chris Cox
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Subspace identification of distributed, decomposable systems

Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference, 2009
This article concerns the identification of a class of linear systems which we call “decomposable systems”. Such systems can be thought of as the interconnection of a number of identical subsystems, and they can be used to model a number of large scale systems.
Paolo Massioni, Michel Verhaegen
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Subspace Identification of Spatially Distributed Systems*

IFAC Proceedings Volumes, 2011
Abstract We propose a new subspace identification algorithm for identifying a class of spatially varying distributed systems. By exploiting the spatial decay of the distributed system, proposed algorithm identifies the local subsystem dynamics from the local-input output data (input-output data of the subsystems in the vicinity of the local subsystem).
A. Haber, P.R. Fraanje, M. Verhaegen
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System Identification via Weighted Subspace Fitting

1992 American Control Conference, 1992
This paper presents a new method for the identification of linear systems parameterised by state space models. The method relies on the concept of subspace-fitting, in which the goal is to find a particular input/output data model parameterized by the state matrices that best fits, in the least-squares sense, the dominant subspace of the measured data.
A. Swindlehurst, R. Roy, B. Ottersten, T. Kailath   +3 more
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Subspace Identification of Pure Stochastic Systems

2006 IEEE International Conference on Automation, Quality and Testing, Robotics, 2006
In this paper we treat the subspace identification of pure stochastic systems with no external input. The stochastic identification problem consists of computing the stochastic system matrices from given output data only. We show how this can be done using geometric operations as orthogonal projections.
D. Sendrescu, D. Popescu, E. Bobasu, E. Petre   +3 more
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Subspace identification of deterministic bilinear systems

Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334), 2000
In this paper, a 'three block' subspace method for the identification of deterministic bilinear systems is developed. The input signal to the system does not have to be white, which is a major advantage over an existing subspace method for bilinear systems.
null Huixin Chen, J. Maciejowski
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Subspace identification of piecewise linear systems

2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601), 2004
Subspace identification can be used to obtain models of piecewise linear state-space systems for which the switching is known. The models should not switch faster than the block size of the Hankel matrices used. The nonconsecutive parts of the input and output data that correspond to one of the local linear systems can be used to obtain the system ...
V. Verdult, M. Verhaegen
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Subspace based identification of unstable systems

2008 IEEE International Conference on Automation, Quality and Testing, Robotics, 2008
This paper deals with subspace-based identification of unstable systems. Because the system is unstable, we will assume the experimental data are generated under stabilizing feedback. In order to remove the effect of noise an orthogonal projection is used. The system state space matrices are recovered from the extended observability matrix.
D. Sendrescu, D. Selisteanu, C. Marin
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Continuous-Time Subspace System Identification Method

Industrial & Engineering Chemistry Research, 2001
We analyze practical problems of discrete-time subspace system identification methods from the control point of view and develop a new continuous-time subspace system identification method. We use a new transform to obtain the derivative information numerically from the measured process output and the process input.
Sung, SW, Lee, SY, Kwak, HJ, Lee, IB
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Uncertainties Quantification for Subspace Identification of Rotating Systems

IFAC Proceedings Volumes, 2013
Abstract The dynamics of rotating systems such as helicopters and wind turbines show periodically time-varying behavior. Differently from the linear time-invariant case, such systems cannot be characterized by the classical modal parametrization. An alternative description is made possible with the Floquet theory which extends the modal analysis and ...
Jhinaoui, Ahmed, Mevel, Laurent, Morlier, Joseph   +2 more
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