Predictive control of nonlinear systems based on fuzzy and neural models

Author

Babuska, R. ; Sousa, J.M. ; Verbruggen, H.B.

Author_Institution

Control Eng. Lab., Delft Univ. of Technol., Delft, Netherlands

fYear

1999

fDate

Aug. 31 1999-Sept. 3 1999

Firstpage

3868

Lastpage

3873

Abstract

An overview of nonlinear predictive control based on neural and fuzzy models is given. The similarities and differences of these two modeling approaches are discussed, as well as their advantages and drawbacks. Several optimization approaches within the predictive controller based on these nonlinear model structures are reviewed, including iterative methods, operating-point and feedback linearization, and discrete search techniques. Some applications are reviewed and an example is given.

Keywords

feedback; fuzzy control; iterative methods; linearisation techniques; neurocontrollers; nonlinear control systems; predictive control; search problems; discrete search techniques; feedback linearization; fuzzy model; iterative method; neural model; nonlinear model structure; nonlinear system; operating-point linearization; optimization approach; predictive control; Computational modeling; Mathematical model; Neural networks; Optimization; Predictive control; Predictive models; Temperature measurement; Nonlinear predictive control; branch and bound methods; fuzzy modeling; linearization; neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference (ECC), 1999 European

Conference_Location

Karlsruhe

Print_ISBN

978-3-9524173-5-5

Type

conf

Filename

7099935