DocumentCode :
1633824
Title :
Stabilizing unstable equilibria using observer-based neural networks with applications in chaos suppression
Author :
Yadmellat, P. ; Nikravesh, S.K.Y.
Author_Institution :
Amirkabir Univ. of Technol., Tehran
fYear :
2009
Firstpage :
96
Lastpage :
103
Abstract :
In this paper, the observer-based stabilization of unstable equilibrium points of a class of unknown nonlinear systems is proposed. The controller is based on feedback linearization where the observer system and control signal are directly estimated by a nonlinear in parameter neural network (NLPNN). A modified back propagation (BP) algorithm with e-modification was used to update the weights of the network. Globally uniformly ultimately boundedness of overall closed-loop system is ensured using Lyapunov´s direct method. To verify the effectiveness of the proposed observer-based controller, a set of simulations was performed on a Rossler and Lorenz chaotic systems.
Keywords :
Lyapunov methods; backpropagation; chaos; closed loop systems; feedback; linearisation techniques; neurocontrollers; nonlinear control systems; observers; stability; Lorenz chaotic system; Lyapunov direct method; Rossler chaotic system; back propagation algorithm; chaos suppression; closed-loop system; control signal; feedback linearization; globally uniformly ultimately boundedness; nonlinear systems; observer-based neural network; unstable equilibria stabilization; Adaptive control; Backstepping; Chaos; Control systems; Linear feedback control systems; Neural networks; Nonlinear control systems; Nonlinear dynamical systems; Nonlinear systems; Programmable control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence in Control and Automation, 2009. CICA 2009. IEEE Symposium on
Conference_Location :
Nashville, TN
Print_ISBN :
978-1-4244-2752-9
Type :
conf
DOI :
10.1109/CICA.2009.4982789
Filename :
4982789
Link To Document :
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