Indirect identification of continuous-time delay systems from step responses

Du, Yan; Tsai, Jason Sheng‐Hong; Patil, Harshal; Shieh, Leang-San; Chen, Yuhua

doi:10.1016/j.apm.2010.07.004

Cited by 33 publications

(15 citation statements)

References 13 publications

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“…These methods require the step response to be relatively noise-free so that the true process response can be seen clearly. Recent and alternative schemes are presented in [20,21].…”

Section: Process Identificationmentioning

confidence: 99%

A spreadsheet method for continuous-time model identification

2013

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“…These methods require the step response to be relatively noise-free so that the true process response can be seen clearly. Recent and alternative schemes are presented in [20,21].…”

Section: Process Identificationmentioning

confidence: 99%

A spreadsheet method for continuous-time model identification

2013

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“…: 1) sensor is fail at some instants; 2) data are missing during transmission; 3) time-scale uncertainly drifts so that data become blank at some important instants; 4) improperly large sampling period is selected, leading to available data is less than that needed by identification; and so on. Although emerging many new algorithms on step identification [8][9][10][11][12], they are also difficult to directly apply for above-mentioned cases.…”

Section: Introductionmentioning

confidence: 97%

Identification of first-order plus dead-time model from less step response data

Luo

Cai

Liu

et al. 2014

2014 9th IEEE Conference on Industrial Electronics and Applications

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This study presents an improved method for firstorder plus dead-time (FOPDT) model identification from less step response data. Firstly, B-spline series expansions are used to approximate step responses, providing effective interpolation values for modeling computation. Then, to enhance modeling accuracy, a least-squares method with a regressive compensation scheme is proposed, which adaptively adjusts error weight coefficients to minimize the response deviation between identified model and actual process. The proposed identification method is not only suitable for cases of less sampling data, but also for those of non-uniform sampling data (where most existing methods of transfer function model identification cannot be applied directly). Simulation results illustrate the effectiveness of the proposed approach.

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“…It is quite challenging to identify a transfer function model with time delay due to the nonlinear relationship between the delay and other model parameters, especially in the presence of measurement noise. Based on step or relay tests, a few synthetic identification methods have been developed for time delay systems [2,8,9]. However, these methods could not deal with colored noise in practice.…”

Section: Introductionmentioning

confidence: 99%