Multiple model approach to linear parameter varying time-delay system identification with EM algorithm

Yang, Xianqiang; Gao, Huijun

doi:10.1016/j.jfranklin.2014.09.015

Cited by 37 publications

(12 citation statements)

References 26 publications

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“…By Schur complement, (15) can be obtained. Equation (14) can be also deduced from (40). Furthermore, by Theorem 1, the composite system (Σ 1 ) is weighted passive.…”

Section: Resultsmentioning

confidence: 92%

“…In addition, time delays generally describe propagation phenomena, material, or energy transfer in intercommoned systems and data transmission in communication systems . They have been the main sources inducing oscillations, instability, and poor control performances.…”

Section: Introductionmentioning

confidence: 99%

“…[34][35][36][37][38] In addition, time delays generally describe propagation phenomena, material, or energy transfer in intercommoned systems and data transmission in communication systems. 39,40 They have been the main sources inducing oscillations, instability, and poor control performances. Stability analysis and controller design of such systems are then of theoretical and practical importance.…”

Section: Introductionmentioning

confidence: 99%

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Feedback passification of switched stochastic time‐delay systems with multiple disturbances via DOBC

Sun

Zong

2019

Intl J Robust & Nonlinear

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Summary This paper is concerned with the problem of feedback passification for switched stochastic time‐delay systems with multiple disturbances subject to mode‐dependent average dwell‐time switching. The multiple disturbances are composed of two parts: one is given through an exogenous system and the other is described in the form of norm‐bounded vector. A disturbance observer is constructed to estimate an exogenous disturbance. Then, a state feedback controller that includes the estimation value is designed to guarantee the passivity of the closed‐loop system. The observer and controller gains are developed via linear matrix inequalities. The effectiveness of the proposed method is verified through a numerical example and an application example to PWM‐driven boost converter.

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“…By Schur complement, (15) can be obtained. Equation (14) can be also deduced from (40). Furthermore, by Theorem 1, the composite system (Σ 1 ) is weighted passive.…”

Section: Resultsmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Feedback passification of switched stochastic time‐delay systems with multiple disturbances via DOBC

Sun

Zong

2019

Intl J Robust & Nonlinear

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“…Recently, the EM algorithm and its variants exhibit their superiority in the identification of hybrid systems with time‐delay. For example, Yang and Gao considered the identification of the LPV systems with unknown constant time‐delay and mode‐related time‐delay. Xie et al solved the identification problem of the linear systems with random time‐delay based on EM algorithm.…”

Section: Introductionmentioning

confidence: 99%

Online identification of time‐delay jump Markov autoregressive exogenous systems with recursive expectation‐maximization algorithm

Chen

Zhao

Liu

2020

Adaptive Control & Signal

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Summary This article considers the identification problem of the jump Markov autoregressive exogenous (JMARX) systems with unknown invariant time‐delay under the framework of recursive expectation‐maximization (REM) algorithm. In this article, a recursive Q‐function is formulated for the JMARX systems, based on which the recursive sufficient statistics are obtained. Then, the parameter vectors, variance, transition probability matrix, and time‐delay are recursively estimated. A numerical example and a simulated continuous fermentation reactor process are employed to illustrate the effectiveness of the proposed algorithm.

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“…However, system identification of linear time-delay systems is a less developed area (Drakunov et al, 2006;Yang and Gao, 2014). Nakagiri and Yamamoto (1995) and Lunel (2001) illustrated the complexity and intractability of the identification of linear time-delay systems.…”

Section: Introductionmentioning

confidence: 99%

Subspace-based identification of discrete time-delay system

Liu

2016

Frontiers Inf Technol Electronic Eng

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We investigate the identification problems of a class of linear stochastic time-delay systems with unknown delayed states in this study. A time-delay system is expressed as a delay differential equation with a single delay in the state vector. We first derive an equivalent linear time-invariant (LTI) system for the time-delay system using a state augmentation technique. Then a conventional subspace identification method is used to estimate augmented system matrices and Kalman state sequences up to a similarity transformation. To obtain a state-space model for the time-delay system, an alternate convex search (ACS) algorithm is presented to find a similarity transformation that takes the identified augmented system back to a form so that the time-delay system can be recovered. Finally, we reconstruct the Kalman state sequences based on the similarity transformation. The time-delay system matrices under the same state-space basis can be recovered from the Kalman state sequences and input-output data by solving two least squares problems. Numerical examples are to show the effectiveness of the proposed method.

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Multiple model approach to linear parameter varying time-delay system identification with EM algorithm

Cited by 37 publications

References 26 publications

Feedback passification of switched stochastic time‐delay systems with multiple disturbances via DOBC

Feedback passification of switched stochastic time‐delay systems with multiple disturbances via DOBC

Online identification of time‐delay jump Markov autoregressive exogenous systems with recursive expectation‐maximization algorithm

Subspace-based identification of discrete time-delay system

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