Identification of Continuous-time Models from Sampled Data

Garnier, Hugues; Wang, Liuping

doi:10.1007/978-1-84800-161-9

Cited by 288 publications

(5 citation statements)

References 40 publications

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“…Meanwhile the sliding length can be set to a positive value smaller than the window size. For each data segment, the Levenberg-Marquardt algorithm [23] and the simplified refined instrumental variable method for continuous-time systems (SRIVC) [24] are used to identify the parameters. Specially, and are identified within first segment.…”

Section: Sliding-window Health Indicator Extractionmentioning

confidence: 99%

“…The stack is integrated with a 24V DC fan for oxygen supply and cooling. The stack operating temperature is controlled by regulating the air fan and the reference varies with temperature as (24) where is the temperature control reference and is the operating current. In the concerned compact fuel cell system, stack temperature, current and stack voltage are measured and analyzed for prognostics.…”

Section: Fc Aging Experiments Under Dynamic Operating Conditionsmentioning

confidence: 99%

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Symbolic deep learning based prognostics for dynamic operating proton exchange membrane fuel cells

Wang

Outbib

et al. 2022

Applied Energy

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Fuel cell (FC) is a promising alternative energy source in a wide range of applications. Due to the unsatisfactory durability performance, FC has not yet been widely used. Prognostics and health management (PHM) has been demonstrated to be an effective solution to enhance the FC durability performance by predicting FC degradation characteristics and adopting health condition based control and maintenance. As the primary task of PHM, prognostics seeks to estimate the remaining useful life (RUL) of FC as early and accurately as possible. However, when FC faces dynamic operating conditions, its degradation characteristics are often hidden in the complex system dynamic behaviors, which makes prognostics challenging. To address this issue, a hybrid prognostics approach is proposed in this paper. Specifically, the health indicator of FC is extracted using a degradation behavior model and sliding-window model identification method. Subsequently, a symbolic-based long short-term memory networks (LSTM) is used to predict the health indicator degradation trend and estimate the RUL. The experimental and simulation results show that the proposed model is able to describe the dynamic behavior of the FC stack voltage and the extracted health indicator show a significant degradation trend. Moreover, health indicator prediction and RUL estimation performance can be improved by deploying the proposed symbolic-based LSTM prognostics model. The proposed approach provides a prognostic horizon approaching 50% of the FC life-cycle, and the average relative accuracy of estimated RUL is close to 90%.

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Section: Sliding-window Health Indicator Extractionmentioning

confidence: 99%

Section: Fc Aging Experiments Under Dynamic Operating Conditionsmentioning

confidence: 99%

Symbolic deep learning based prognostics for dynamic operating proton exchange membrane fuel cells

Wang

Outbib

et al. 2022

Applied Energy

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“…3. The third one, standard when a local approach is undertaken, aims at identifying consistent local black-box LTI models by using dedicated toolboxes and well-known techniques such as the ones available in [21,38] for DT models or in [10] for CT models. 4.…”

Section: Introductionmentioning

confidence: 99%

Combining Analytic and Experimental Information for Linear Parameter-Varying Model Identification: Application to a Flexible Robotic Manipulator

Vizer¹,

Mercère²,

Prot³

et al. 2014

Period. Polytech. Elec. Eng. Comp. Sci.

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Identifying a linear parameter-varying (LPV) model of a nonlinear system from local experimental data is still a problem which deserves attention. Many difficulties related to the deter- Keywords linear parameter-varying model · analytic modeling · robotic manipulator · non-smooth cost function · numerical optimization IntroductionWhen models of robotic systems are required, a standard solution consists in resorting to a white-box model obtained by combining the laws of physics governing the behavior of the system. Their descriptions are usually based on the EulerLagrange equations and the virtual work principle [9]. Interesting from a theoretical point of view, the exclusive use of the standard laws of physics makes the final model quite complex and requires an accurate knowledge of the manipulator as well as high-level skills in robotics especially when different robot structures are handled. This is all the more true when the user wants to have access to physical parameters of the system which are imperfectly known. In order to circumvent these difficulties, efforts dedicated to robot identification are more and more made in industry [17]. However, a direct identification of a non-linear black-box model is often complicated because• strong non-linearities may vary with the working conditions can appear, • the development of a global non-linear model structure can rely on strong assumptions such as a uniform density of the manipulator segments or the nature of the deformations if any.

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“…The literature on parameter identification is extensive. For general textbooks on the subject from the control area the reader is referred to Aström and Eykhoff (1971); Garnier and Wang (2008);Isermann (2013aIsermann ( , 2013b; Ljung (1999); Unbehauen and Rao (1987), for instance. Despite the long standing history of the topic, some theoretical questions remain open and new approaches are still developed.…”

mentioning

confidence: 99%

Contributions to numerical differentiation using orthogonal polynomials and its application to fault detection and parameter identification

Othmane

2023

At - Automatisierungstechnik

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Identification of Continuous-time Models from Sampled Data

Cited by 288 publications

References 40 publications

Symbolic deep learning based prognostics for dynamic operating proton exchange membrane fuel cells

Symbolic deep learning based prognostics for dynamic operating proton exchange membrane fuel cells

Combining Analytic and Experimental Information for Linear Parameter-Varying Model Identification: Application to a Flexible Robotic Manipulator

Contributions to numerical differentiation using orthogonal polynomials and its application to fault detection and parameter identification

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