Results 121 to 130 of about 7,261 (147)

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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Weighting in Subspace-Based System Identification

IFAC Proceedings Volumes, 2000
Abstract Subspace-based methods for system identification are often based on an estimate of the range space of the extended observability matrix. It is thus of great interest to investigate, and also optimize, the accuracy of the estimated subspace. Especially, the influence of certain weighting matrices is an unresolved issue.
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Subspace identification of hammerstein systems using B-splines

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012
This paper presents an algorithm for the identification of Hammerstein cascades with hard nonlinearities. The nonlinearity of the cascade is described using a B-spline basis with fixed knot locations; the linear dynamics are described using a state-space model. The algorithm automatically estimates both the order of the linear system and the number and
K, Jalaleddini, D T, Westwick, R E, Kearney   +2 more
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Subspace-based state-space system identification

Circuits, Systems, and Signal Processing, 2002
The subspace approach to state-space modeling offers numerically reliable algorithms for computing state-space descriptions directly from data. The methods are competitive with respect to traditional prediction-error or instrumental variable techniques, in particular for the high-order multi-input multi-output case.
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