Paper Info
Title
Indirect Adaptive Model Predictive Control Supervised By Fuzzy Logic
Abstract
In order to get correct results using the model predictive control, one must find the suitable values of its tuning parameters. In many case this task is achieved by empirical methods. It's sufficient to control a physical system which internal parameters remain the same. The chosen controller parameters continue to provide satisfactory performances against small system variations. They become quickly inappropriate when strong variations occur in the process. One of the classical approaches consists to identify online the system model to update its controller based on this model. This solution doesn't ensure always best results. In this paper, we show how one can perform a suitable control to a system with variable parameters applying a fuzzy-logic-supervised predictive control. The fuzzy logic supervisor fulfills the online tuning of the predictive control parameters. Thus, we carry out a comparison between these two strategies.
Year
DOI
Venue
2011
10.1109/FUZZY.2011.6007612
IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ 2011)
Keywords
Field
DocType
MPC, Augmented state space model, Fuzzy Logic Supervisor, LTV system
Supervisor,Control theory,Computer science,Control theory,Physical system,Fuzzy logic,Model predictive control,Artificial intelligence,Empirical research,System model,Machine learning
Conference
ISSN
Citations 
PageRank 
1098-7584
0
0.34
References 
Authors
3
2
Name
Order
Citations
PageRank
Jerry Mamboundou101.01
Nicolas Langlois22612.61