A model-free predictive control method based on polynomial regression

• Published in: 2016 SICE International Symposium on Control Systems (ISCS) pp. 1 - 6
• Main Authors: Hongran Li, Yamamoto, Shigeru
• Format: Conference Proceeding
• Language: English
• 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
• DOI: 10.1109/SICEISCS.2016.7470167

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.