članak: 1 od 1  
Serbian Journal of Electrical Engineering
2011, vol. 8, br. 3, str. 307-323
jezik rada: engleski
neklasifikovan
doi:10.2298/SJEE1103307B
Neural feedback linearization adaptive control for affine nonlinear systems based on neural network estimator
(naslov ne postoji na srpskom)
Bahita Mohameda, Belarbi Khaledb
aFaculty of Hydrocarbons and Chemistry (FHC), University of Boumerdes, Boumerdes, Algeria
bFaculty of Engineering, University of Constantine, Constantine, Algeria

e-adresa: mbahita@yahoo.fr, kbelarbi@yahoo.com

Sažetak

(ne postoji na srpskom)
In this work, we introduce an adaptive neural network controller for a class of nonlinear systems. The approach uses two Radial Basis Functions, RBF networks. The first RBF network is used to approximate the ideal control law which cannot be implemented since the dynamics of the system are unknown. The second RBF network is used for on-line estimating the control gain which is a nonlinear and unknown function of the states. The updating laws for the combined estimator and controller are derived through Lyapunov analysis. Asymptotic stability is established with the tracking errors converging to a neighborhood of the origin. Finally, the proposed method is applied to control and stabilize the inverted pendulum system.

Ključne reči

adaptive control; control gain estimation; feedback linearization; radial basis function network

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