DocumentCode :
3077454
Title :
Modeling and control of unknown chaotic systems via multiple models
Author :
Cho, Jeongho ; Lan, Jing ; Principe, Jose C. ; Motter, Mark A.
Author_Institution :
Comput. Neuroeng. Lab., Florida Univ., Gainesville, FL
fYear :
2004
fDate :
Sept. 29 2004-Oct. 1 2004
Firstpage :
53
Lastpage :
62
Abstract :
This paper presents a multiple model based control methodology with sliding manifold for unknown chaotic systems from a finite number of measurements of the inputs and outputs. The self-organizing map (SOM) is employed to quantize the operating regions such that a local linear model is built for each region in which a sliding mode controller is designed based on the model. Switching of the controllers is done synchronously with the active local linear model that tracks the different operating conditions. It is the purpose of this work to regulate the unknown chaos to a fixed point or a stable periodic orbit. Simulations on the unknown controlled Lorenz system illustrate the efficiency of the proposed control technique
Keywords :
chaos; control system synthesis; linear systems; nonlinear control systems; self-organising feature maps; uncertain systems; variable structure systems; local linear model; multiple model based control methodology; self-organizing map; sliding manifold; sliding mode controller; stable periodic orbit; unknown chaotic systems; unknown controlled Lorenz system; Chaos; Control system synthesis; Control systems; NASA; Neural engineering; Nonlinear control systems; Nonlinear dynamical systems; Nonlinear systems; Sliding mode control; State-space methods;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning for Signal Processing, 2004. Proceedings of the 2004 14th IEEE Signal Processing Society Workshop
Conference_Location :
Sao Luis
ISSN :
1551-2541
Print_ISBN :
0-7803-8608-4
Type :
conf
DOI :
10.1109/MLSP.2004.1422959
Filename :
1422959
Link To Document :
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