Model-Free Predictive Control Using Polynomial Regressors. Issue 2 (1st March 2017)

• Record Type: Journal Article
• Authors: Li, Hongran; Yamamoto, Shigeru
• Is Part Of: SICE journal of control, measurement, and system integration. Volume 10:Issue 2(2017)
• Page Start: 93
• Page End: 99
• Publication Date: 2017-03-01
• Subjects: data-driven control -- predictive control -- just-in-time modeling -- polynomial regression -- nonlinear systems
• DOI: 10.9746/jcmsi.10.93
• Languages: English
• ISSNs: 1882-4889

Abstract

Abstract : Model-free predictive control directly computes the control input from massive input/output datasets and does not use a mathematical model. In contrast, conventional model predictive control relies on mathematical models. Although the underlying principle of model-free predictive control utilizes linear regression vectors comprising input/output data, it can also be applied to control nonlinear systems. In this study, the linear regression vectors are extended to polynomial regression vectors, improving the control performance. Using numerical simulations, we demonstrate the effectiveness of this approach.