Neural network adaptive state feedback control of a magnetic levitation system

Magnetic levitation system is a typical nonlinear and instable system. Based on the complexity and inaccuracy of modelling, in this paper identified magnetic levitation system using the speciality that neural network(NN) can approach any nonlinear function. A Radial Basis Function neural network (RBFNN) controller is designed based on the neural network adaptive control principle. This paper proposes a control method which combine neural network adaptive control method and state feedback control method based on RBFNN. A simulation of the system is proposed, and the result shows that RBFNN could approach magnetic levitation system very well, neural network adaptive state feedback controller has a good effect on this nonlinear system; this control system has a preferable stability and control property.

[1]  Lorenzo Marconi,et al.  Robust nonlinear disturbance suppression of a magnetic levitation system , 2003, Autom..

[2]  Chun-Fei Hsu,et al.  Indirect adaptive self-organizing RBF neural controller design with a dynamical training approach , 2012, Expert Syst. Appl..

[3]  Syuan-Yi Chen,et al.  Robust Dynamic Sliding-Mode Control Using Adaptive RENN for Magnetic Levitation System , 2009, IEEE Transactions on Neural Networks.

[4]  Zi-Jiang Yang,et al.  Adaptive robust nonlinear control of a magnetic levitation system , 2001, Autom..

[5]  J. Qiao,et al.  Prediction of activated sludge bulking based on a self-organizing RBF neural network☆ , 2012 .

[6]  Karl Henrik Johansson,et al.  Optimal structured static state-feedback control design with limited model information for fully-actuated systems , 2011, Autom..

[7]  Yana Yang,et al.  Neural network-based adaptive position tracking control for bilateral teleoperation under constant time delay , 2013, Neurocomputing.

[8]  Tao Li,et al.  Adaptive RBF neural-networks control for a class of time-delay nonlinear systems , 2008, Neurocomputing.

[9]  Selami Beyhan,et al.  Stable modeling based control methods using a new RBF network. , 2010, ISA transactions.

[10]  Nabil Abdel-Jabbar,et al.  State estimation and state feedback control for continuous fluidized bed dryers , 2005 .

[11]  Niu Lin,et al.  A direct adaptive neural-network control of nonlinear systems , 2000, Proceedings of the 3rd World Congress on Intelligent Control and Automation (Cat. No.00EX393).