Identification of the delay parameter for nonlinear time-delay systems with unknown inputs

Zheng, Gang; Barbot, Jean–Pierre; Boutat, Driss

doi:10.1016/j.automatica.2013.02.020

Cited by 40 publications

(19 citation statements)

References 26 publications

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“…For example, how does the choice of sample times influence estimation accuracy, and what properties should the functions f (system dynamics) and g (output function) satisfy to guarantee high-quality estimates? The second question is related to the socalled identifiability issue, which has been studied extensively in the literature (see Denis-Vidal, Jauberthie, & Joly-Blanchard, 2006, Lunel, 2001, Zheng et al, 2013, and the references cited therein). One of the limitations of the new estimation method proposed in this paper is that it relies on the statistical properties of the output noise.…”

Section: Resultsmentioning

confidence: 99%

“…Parameter estimation for time-delay systems has attracted considerable research interest over the past two decades (see Belkoura, Richard, & Fliess, 2009, Drakunov, Perruquetti, Richard, & Belkoura, 2006, Lunel, 2001, Orlov, Belkoura, Richard, & Dambrine, 2002, 2003, Park, Han, & Kwon, 2013, Tuch, Feuer, & Palmor, 1994and Zheng, Barbot, & Boutat, 2013. Popular approaches for solving the parameter estimation problem include swarm intelligence algorithms such as particle swarm optimization (Gao, Qi, Yin, & Xiao, 2010;Tang & Guan, 2009), or finite-dimensional approximation schemes for the original infinite-dimensional timedelay model (Banks, Rehm, & Sutton, 2010).…”

Section: Introductionmentioning

confidence: 99%

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Parameter estimation for nonlinear time-delay systems with noisy output measurements

Lin

Loxton

et al. 2015

Automatica

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a b s t r a c tThis paper considers the problem of using noisy output data to estimate unknown time-delays and unknown system parameters in a general nonlinear time-delay system. We formulate the problem as a dynamic optimization problem in which the unknown quantities are decision variables to be chosen optimally, with the cost function penalizing the mean and variance of the least-squares error between actual and predicted system output. Since the time-delays and system parameters influence the cost function implicitly through the governing time-delay system, the cost function's gradient -which is required to solve the problem using gradient-based optimization techniques -cannot be computed analytically using standard differentiation rules. We instead develop two computational methods for evaluating this gradient: one involves solving an auxiliary time-delay system forward in time; the other involves solving an auxiliary time-advance system backward in time. On this basis, we propose an efficient optimization algorithm for determining optimal estimates for the time-delays and system parameters. We conclude the paper by examining the performance of this algorithm on a dynamic model of a continuously-stirred tank reactor.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parameter estimation for nonlinear time-delay systems with noisy output measurements

Lin

Loxton

et al. 2015

Automatica

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“…Many results have been published to treat this kind of systems for different aspects, such as stability (Fridman, 2014), observability (Zheng, Barbot, Boutat, Floquet, & Richard, 2011) and identifiability (Zheng, Barbot, & Boutat, 2013).…”

Section: Introductionmentioning

confidence: 99%

Unknown input observer for linear time-delay systems

Zheng

Bejarano²,

Perruquetti

et al. 2015

Automatica

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International audienceThis paper investigates an unknown input observer design for a large class of linear systems with unknown inputs and commensurate delays. A Luenberger-like observer is proposed by involving only the past and actual values of the system output. The required conditions for the proposed observer are considerably relaxed in the sense that they coincide with the necessary and sufficient conditions for the unknown input observer design of linear systems without delays

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“…Some methods are based on the lifting technique [11], the auxiliary model [8] and the multi-innovation theory [7]. Most [2,17,34], and this motivates us to study novel identification methods for nonlinear systems to meet the requirement of industrial development.…”

Section: Introductionmentioning

confidence: 99%

Data Filtering-Based Multi-innovation Stochastic Gradient Algorithm for Nonlinear Output Error Autoregressive Systems

Mao

Ding

2015

Circuits Syst Signal Process

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This paper discusses the parameter estimation problems of nonlinear output error autoregressive systems and presents a data filtering-based multi-innovation stochastic gradient algorithm for improving the parameter estimation accuracy of the stochastic gradient algorithm by combining the multi-innovation identification theory and the data filtering technique. The proposed algorithm is effective and can generate highly accurate parameter estimates compared with the multi-innovation stochastic gradient algorithm. The simulation results confirm this conclusion.

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Identification of the delay parameter for nonlinear time-delay systems with unknown inputs

Cited by 40 publications

References 26 publications

Parameter estimation for nonlinear time-delay systems with noisy output measurements

Parameter estimation for nonlinear time-delay systems with noisy output measurements

Unknown input observer for linear time-delay systems

Data Filtering-Based Multi-innovation Stochastic Gradient Algorithm for Nonlinear Output Error Autoregressive Systems

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