Journal Article10.1007/BF02706372
Process Identification for an SOPDT Model Using Rectangular Pulse Input
TL;DR: The robustness of the estimation in noisy process is proved from the investigation of the performance in the processes having various levels of noise.
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Abstract: A new method of process identification for a second-order-plus-dead-time model is proposed and tested with two example systems. In the activation of the example processes for the identification, a rectangular pulse input is applied to open loop systems. The model parameters are estimated by minimizing sum of modeling errors with the least squares method. The estimation performance is examined by comparing the output pulse responses from the example system and the estimated model. The performance comparison of the proposed method and two existing techniques indicates that satisfactory parameter estimation is available from the proposed procedure. In addition, the role of sampling time and the shape of input pulse is evaluated and it is found that the sampling time of less than 0.01 minute gives good estimation while the shape of input pulse does not affect the estimation performance. Finally, the robustness of the estimation in noisy process is proved from the investigation of the performance in the processes having various levels of noise.
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Citations
Estimation of First Order Plus Dead Time and Second Order Plus Dead Time models from noisy step response data
Anca Maxim,Robin De Keyser +1 more
TL;DR: In this paper , the estimation of first-order plus-dead-time (FOPDT) and second-order-plus-deadtime (SOPDT), from noisy step response data, is proposed.
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References
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Karl Johan Åström,Tore Hägglund +1 more
TL;DR: A simple method for estimating the critical gain and the critical frequency is described, which may be used for automatic tuning of simple regulators as well as initialization of more complicated adaptive regulators.
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Least squares parameter estimation of continuous-time ARX models from discrete-time data
TL;DR: It is shown that if the highest order derivative is selected with care, a least squares estimate will be accurate and this theoretical analysis is complemented by some numerical examples which provide further insight into the choice of derivative approximation.
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Identification of continuous-time models
TL;DR: An operator transformation is introduced that allows for keeping a continuous-time parametrization whereas the parameter estimation can be made by means of a discrete-time maximum-likelihood algorithm.
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Identification of continuous-time models
Rolf Johansson
- 16 Dec 1992
TL;DR: An operator transformation that allows for keeping a continuous-time parametrization is introduced and the parameter estimation can be made by means of a discrete-time maximum-likelihood algorithm.
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