Title :
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
fDate :
Aug. 31 1999-Sept. 3 1999
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;
Conference_Titel :
Control Conference (ECC), 1999 European
Conference_Location :
Karlsruhe
Print_ISBN :
978-3-9524173-5-5
