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A method to construct a reference model for model reference adaptive control
Author(s) -
Su Shi-jie,
Zhu Yuan-yuan,
Wang Hai-rong,
Yun Chen
Publication year - 2019
Publication title -
advances in mechanical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.318
H-Index - 40
eISSN - 1687-8140
pISSN - 1687-8132
DOI - 10.1177/1687814019890455
Subject(s) - reference model , construct (python library) , transfer function , control theory (sociology) , nonlinear system , frequency response , computer science , function (biology) , control (management) , engineering , artificial intelligence , programming language , biology , electrical engineering , evolutionary biology , software engineering , quantum mechanics , physics
Model reference adaptive control takes the output of a reference model as the target and ensures that the actual output is consistent with the target. In this scenario, the reference model determines the performance of the control system. Constructing a reference model with specific performance is challenging, and most researchers construct the reference model by experience. As alternative frameworks, we propose two novel reference model construction methods, namely, zero-pole method and frequency response method. The zero-pole method establishes a nonlinear optimization model to ensure that the added zeros and poles are as far away from the imaginary axis as possible. In the case where this cannot be done, the frequency response method matches the system frequency response of the reference model and the given model to the extent possible using a nonlinear optimization model. Experimental results are presented to confirm that both methods can construct a reference model based on a given transfer function. Deviations in response between the reference models constructed using the two methods and the given model are compared, and the reasons for higher accuracy of the frequency response method are discussed.

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