Nonlinear System Identification of a Jointed Structure Using Full-Field Data: Part II Analysis

Kosova, Giancarlo; Jin, Mengshi; Cenedese, Mattia; Chen, Wei; Singh, Aryan; Jana, Debasish; Brake, Matthew Robert; Schwingshackl, Christoph W.; Nagarajaiah, Satish; Moore, Kate; Noël, Jean-Philippe

doi:10.1007/978-3-030-47626-7_27

Cited by 3 publications

(4 citation statements)

References 6 publications

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“…where 𝜽 0 denotes the true parameter vector. Thus, the SG formula Equation (10) has the following form…”

Section: Bias Compensated Sg (Bc-sg) Algorithmmentioning

confidence: 99%

“…There exist various types of nonlinear systems in engineering. Therefore, the identification of nonlinear systems is very important and has attracted continuous attention of researchers 9‐11 . To describe different nonlinear systems, many nonlinear structures have been developed, such as NARMAX (nonlinear autoRegressive moving average model with exogenous inputs) model, Volterra series model, block‐oriented nonlinear model, and so forth 12‐14 .…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the identification of nonlinear systems is very important and has attracted continuous attention of researchers. [9][10][11] To describe different nonlinear systems, many nonlinear structures have been developed, such as NARMAX (nonlinear autoRegressive moving average model with exogenous inputs) model, Volterra series model, block-oriented nonlinear model, and so forth. [12][13][14] In recent years, a nonlinear rational model (NRM) has been gradually applied in the modeling and control of nonlinear systems, particularly in some chemical processes and mechanistic systems.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Bias compensated stochastic gradient algorithm for identification of an ARX‐type nonlinear rational model and its application in modeling of the dynamic of the cellular toxicity

Jing

Kiely

Luxton

2022

Intl J Robust & Nonlinear

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Nonlinear rational model (NRM) is a generalized nonlinear model, the NAR-MAX model and Volterra model can be regarded as its special cases. In this article, the parameter identification of a class of nonlinear rational models is studied. Due to the coupling of the model output and the information vector, the parameter identification of the NRM is very challenging. To reduce the complexity of the identification, the stochastic gradient algorithm is used. However, the estimate given by traditional stochastic gradient algorithm is biased. To obtain unbiased estimation, the bias is calculated by using the observations and the previous estimate and then compensated to the biased estimate. A variable factor considering the estimation error is introduced to speed up the algorithm. Theoretical analysis shows that the proposed algorithm can obtain an unbiased estimate and has the complexity of O(n). The proposed algorithm is validated by a numerical example and is applied in modeling the dynamics of the cellular toxicity using the tetra-ethyl ammonium chloride. Results show that the proposed algorithm can obtain an accurate estimate for the nonlinear rational model with less computation.

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“…where 𝜽 0 denotes the true parameter vector. Thus, the SG formula Equation (10) has the following form…”

Section: Bias Compensated Sg (Bc-sg) Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Bias compensated stochastic gradient algorithm for identification of an ARX‐type nonlinear rational model and its application in modeling of the dynamic of the cellular toxicity

Jing

Kiely

Luxton

2022

Intl J Robust & Nonlinear

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“…Mechanical joints have a significant impact on the response of built-up structures. A critical amount of nonlinearity is introduced into the dynamics because of friction, wear, and non-idealized boundary conditions [1]. In fact, the stiffness and the dissipative characteristics resulting from slip-stick friction interactions inside the joint effectively depend upon the contact area.…”

Section: Introductionmentioning

confidence: 99%

Joint Interface Contact Area Predictions Using Surface Strain Measurements

Singh

Moore

2021

Nonlinear Structures &Amp; Systems, Volume 1

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Bolted joints are vital constituents of almost every small-to large-scale built-up structure. An especially important aspect of bolted joint modeling is the prediction of the contact area inside the interface of the joint based on the torque applied to the joint and surface characteristics. This is because the joint interface contact area effectively determines the stiffness of the joint as well as the dissipative qualities resulting from slip-stick friction interactions. This research introduces a novel method of estimating actual contact areas inside the interfaces of bolted joints by measuring the strains on the external surfaces induced by the tightening of a bolt. This method can determine contact areas without directly interfering with or altering the interface like existing methods. The experimental specimen used to demonstrate the technique consists of two flat plates with a carriage bolt on the top plate. A carriage bolt instead of a conventional bolt has been used to facilitate surface strain measurements, measured using the digital image correlation measurement technique. Contact areas inside the interface are measured using pressure-sensitive films, and the torque is measured using a digital torque wrench.

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Nonlinear System Identification with Multiple Data Sets for Structures with Bolted Joints

Blackham,

Spits,

Lengger

et al. 2024

Conference Proceedings of the Society for Experimental Mechanics Series

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Nonlinear System Identification of a Jointed Structure Using Full-Field Data: Part II Analysis

Cited by 3 publications

References 6 publications

Bias compensated stochastic gradient algorithm for identification of an ARX‐type nonlinear rational model and its application in modeling of the dynamic of the cellular toxicity

Bias compensated stochastic gradient algorithm for identification of an ARX‐type nonlinear rational model and its application in modeling of the dynamic of the cellular toxicity

Joint Interface Contact Area Predictions Using Surface Strain Measurements

Nonlinear System Identification with Multiple Data Sets for Structures with Bolted Joints

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