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MATLAB's Implementation of A Simple Analytical Method for Estimation of the Five-Parameter Model: Second-Order with Zero Plus Time Delay.

Authors: Tomaž Kos; Damir Vrančić;

MATLAB's Implementation of A Simple Analytical Method for Estimation of the Five-Parameter Model: Second-Order with Zero Plus Time Delay.

Abstract

MATLAB's Implementation of A Simple Analytical Method for Estimation of the Five-Parameter Model: Second-Order with Zero Plus Time Delay. The algorithms are presented in the paper T. Kos and D. Vrančić, “A Simple Analytical Method for Estimation of the Five-Parameter Model: Second-Order with Zero Plus Time Delay,” Mathematics, vol. 9, no. 14, p. 1707, Jul. 2021. Abstract: Process models play an important role in the process industry. They are used for simulation purposes, quality control, fault detection, and control design. Many researchers have been engaged in model identification. However, it is difficult to find an analytical identification method that provides a good model and requires a relatively simple experiment. This is the advantage of the method of moments. In this paper, an analytical method based on the measurement of the process moments (characteristic areas) is proposed, to identify the five-parameter model (second-order process with zero plus time delay) from either the closed-loop or open-loop time responses of the process (in the time-domain), or the general-order transfer function with time delay (in the frequency-domain). The only parameter required by the user is the type of process (minimum phase or non-minimum phase process), which in practice can be easily determined from the time response of the process. The method can also be used to reduce the higher-order process model. The proposed identification method was tested on several illustrative examples, and compared to other identification methods. The comparison with existing methods showed the superiority of the proposed method. Moreover, the tests confirmed that the algorithm of the proposed method works properly for a wide family of process models, even in the presence of moderate process noise. For the newest version of the Matlab code see JSI GitLab. Authors: Tomaž Kos and Damir Vrančić, Department of Systems and Control, Jozef Stefan Institute (JSI), Jamova cesta 39, 1000 Ljubljana, Slovenia Installation Implemented code was tested in MATLAB version 2018b. No additional installation is needed. Just run one of the main scripts (*.m files in the root directory of the code). Description of the main MATLAB scripts used in paper Algorithm.m This .m file obtains equations for second-order (five-parameter) process model identification from the characteristics areas (process models). Used in Section 3 of the paper. Test_frequency_domain.m This .m file tests the proposed model identification method in the frequency-domain (identification of the model from a general-order transfer function with a time delay). Used in Section 4.1 of the paper. Test_time_domain.m This .m file tests the proposed model identification method in the time-domain (identification from an open-loop time response of a process). Used in Section 4.2 of the paper. Test_time_domain_noise.m This .m file tests the proposed model identification method in the time-domain under process noise. Used in Section 4.2 of the paper.

Keywords

Process model, Process identification, Process model reduction, Delayed processes, Model fitting, Process moments

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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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