Results 181 to 190 of about 3,247,690 (230)

Fixed point iteration‐based subspace identification of Hammerstein state‐space models

IET Control Theory & Applications, 2019
In this study, a fixed point iteration-based subspace identification method is proposed for Hammerstein state-space systems. The original system is decomposed into two subsystems with fewer parameters based on the hierarchical identification principle ...
Jie Hou, Fengwei Chen, Penghua Li, Zhiqin Zhu   +3 more
semanticscholar   +1 more source

Closed loop subspace system identification

Proceedings of the 36th IEEE Conference on Decision and Control, 2002
We present a general framework for closed loop subspace system identification. This framework consists of two new projection theorems which allow the extraction of non-steady state Kalman filter states and of system related matrices directly from input output data.
P. Van Overschee, B. De Moor
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Nonlinear modeling of PEMFC based on fractional order subspace identification

Asian journal of control, 2019
Aiming at the multivariable, nonlinear and fractional‐order characteristics of proton exchange membrane fuel cell (PEMFC), this paper presents a nonlinear state space model based on a novel fractional Hammerstein model subspace identification theory.
Zhidong Qi, Qi Sun, Weiping Ge, Yongkang He   +3 more
semanticscholar   +1 more source

Open-loop Subspace Identification

2008
Conventionally, a system is modeled by a transfer function, which is a fractional representation of two polynomials with real coefficients, identified using an optimization scheme for a nonlinear least-squares fit to the data, as discussed in Chapter 2.
Biao Huang, Ramesh Kadali
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Improved closed-loop subspace identification based on principal component analysis and prior information

Journal of Process Control, 2019
Subspace identification is a very useful tool for estimating a state-space model for a dynamic system. However, most of the subspace identification methods (SIMs) can only provide consistent estimations when the quality of the data is good.
Ling Zhang, Donghua Zhou, M. Zhong, Youqing Wang   +3 more
semanticscholar   +1 more source

Subspace identification of Hammerstein-type nonlinear systems subject to unknown periodic disturbance

International Journal of Control, 2019
In this paper, a subspace identification method is proposed for Hammerstein-type nonlinear systems subject to periodic disturbances with unknown waveforms.
Jie Hou, Tao Liu, Qing‐Guo Wang
semanticscholar   +1 more source

Prior-knowledge-based subspace identification for batch processes

Journal of Process Control, 2019
In this paper, a prior-knowledge-based subspace identification method (SIM) is proposed for batch processes subject to repeatable disturbances. The proposed method is a two-step procedure for state-space model identification: in the first step, the ...
Jie Hou, Fengwei Chen, Li Penghua, Zhiqin Zhu   +3 more
semanticscholar   +1 more source

On-line subspace system identification

Control Engineering Practice, 1993
Abstract Although state space identification techniques offer some unique advantages over traditional system identification methods based on input/output transfer functions, the computational burden of state space subspace identification has prevented its real-time application. The major costs result from the need for the singular value (or sometimes
Y.M. Cho, G. Xu, T. Kailath
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Closed-loop Subspace Identification

2008
The problem of closed-loop identification has been investigated for over 30 years. Important issues such as identifiability under closed-loop conditions have received attention by many researchers [75, 76, 77, 10]. A number of identification strategies have been developed [11, 10].
Biao Huang, Ramesh Kadali
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Direct multivariate subspace time identification

Mechanical Systems and Signal Processing, 2010
Abstract The paper presents a time domain method to identify structural modal parameters by fitting a discrete multivariate space-time model into noise corrupted, input–output measurement data. The subspace identification scheme proposed is an important characteristic of the method, leading to a deterministic and statistically bias free estimation of
Paulo R.G. Kurka, Simon Braun
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