Adaptive RBF Neural Vibration Control of Flexible Structure | Zendy

Tongyue Hu | Zendy; Juntao Fei | Zendy

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Adaptive RBF Neural Vibration Control of Flexible Structure

Author(s) -

Tongyue Hu,

Juntao Fei

Publication year - 2014

Publication title -

advances in mechanical engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.318

H-Index - 40

eISSN - 1687-8140

pISSN - 1687-8132

DOI - 10.1155/2014/956026

Subject(s) - control theory (sociology) , artificial neural network , controller (irrigation) , cantilever , radial basis function , lyapunov stability , vibration , computer science , lyapunov function , adaptive control , bounded function , sliding mode control , vibration control , engineering , nonlinear system , mathematics , artificial intelligence , control (management) , physics , structural engineering , mathematical analysis , quantum mechanics , agronomy , biology

An adaptive sliding mode controller using radial basis function (RBF) network is proposed to approximate the unknown system dynamics for cantilever beam. Neural network controller is designed to approximate the unknown system model. In the presence of unknown model uncertainties and external disturbances, sliding mode controller is employed to compensate for such system nonlinearities and improve the tracking performance. Online neural network (NN) weight tuning algorithms are designed based on Lyapunov stability theory, which can guarantee bounded tracking errors as well as bounded NN weights. Numerical simulation for cantilever beam is investigated to verify the effectiveness of the proposed adaptive neural control scheme and demonstrate the satisfactory vibration suppression performance

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