Neural feedback linearization adaptive control for affine nonlinear systems based on neural network estimator | Zendy

Mohamed Bahita | Zendy; Khaled Belarbi | Zendy

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Neural feedback linearization adaptive control for affine nonlinear systems based on neural network estimator

Author(s) -

Mohamed Bahita,

Khaled Belarbi

Publication year - 2011

Publication title -

serbian journal of electrical engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.133

H-Index - 5

eISSN - 2217-7183

pISSN - 1451-4869

DOI - 10.2298/sjee1103307b

Subject(s) - control theory (sociology) , artificial neural network , nonlinear system , inverted pendulum , lyapunov function , radial basis function , feedback linearization , estimator , controller (irrigation) , adaptive control , affine transformation , computer science , mathematics , artificial intelligence , control (management) , statistics , physics , quantum mechanics , pure mathematics , agronomy , biology

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

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