A model-free predictive control method based on polynomial regression

This paper proposes a model-free predictive control method for nonlinear systems on the basis of polynomial regression. In contrast to conventional model predictive control, model-free predictive control does not require mathematical models. Instead, it uses the previous recorded input/output datase...

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Bibliographic Details
Published in2016 SICE International Symposium on Control Systems (ISCS) pp. 1 - 6
Main Authors Hongran Li, Yamamoto, Shigeru
Format Conference Proceeding
LanguageEnglish
Published The Society of Instrument and Control Engineers - SICE 13.05.2016
Subjects
Adaptation models
Computational modeling
Data models
data-driven control
just-in-time modeling
Mathematical model
Numerical models
polynomial regression
Predictive control
Predictive models
Volterra series
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DOI10.1109/SICEISCS.2016.7470167

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Summary:This paper proposes a model-free predictive control method for nonlinear systems on the basis of polynomial regression. In contrast to conventional model predictive control, model-free predictive control does not require mathematical models. Instead, it uses the previous recorded input/output datasets of the controlled system to predict an optimal control input so as to achieve the desired output. The novel point in this paper is the improvement of existing model-free predictive control by adopting polynomial regression, which is a generalization of the so-called Volterra series expansion of nonlinear functions.
DOI:10.1109/SICEISCS.2016.7470167