DocumentCode :
3161586
Title :
Adaptive Reference-Augmented Predictive Control
Author :
Bottasso, Carlo L. ; Nicastro, Roberto ; Savini, Barbara ; Riviello, Luca
Author_Institution :
Politecnico di Milano, Milano
fYear :
2007
fDate :
9-13 July 2007
Firstpage :
3769
Lastpage :
3774
Abstract :
We describe a novel adaptive non-linear model predictive controller which is based on the idea of neural- augmentation of reference elements, both at the level of the reduced model and at the level of the control action. The new methodology is primarily motivated by the desire to consistently incorporate existing legacy modeling and control techniques into an adaptive non-linear, yet real-time-capable, control framework. The proposed procedures are demonstrated in a virtual environment with the help of the classical model problem of the double inverted-pendulum, and with the more challenging reflexive control of an autonomous helicopter.
Keywords :
adaptive control; neurocontrollers; nonlinear control systems; predictive control; reduced order systems; adaptive nonlinear model predictive controller; legacy modeling; neural reference augmentation; reduced model; Adaptive control; Cities and towns; Helicopters; Neural networks; Open loop systems; Optimal control; Predictive control; Predictive models; Programmable control; Virtual environment;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
American Control Conference, 2007. ACC '07
Conference_Location :
New York, NY
ISSN :
0743-1619
Print_ISBN :
1-4244-0988-8
Electronic_ISBN :
0743-1619
Type :
conf
DOI :
10.1109/ACC.2007.4282331
Filename :
4282331
Link To Document :
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