Title :
Sliding Mode Control of Magnetic Levitation System Using Radial Basis Function Neural Networks
Author :
Aliasghary, M. ; Jalilvand, A. ; Teshnehlab, M. ; Shoorehdeli, M. Aliyari
Author_Institution :
Electr. Eng. Dept., Sci. & Res. Branch of Islamic Azad Univ., Tehran
Abstract :
This paper has developed a sliding mode controller (SMC) based on a radial basis function model for control of magnetic levitation system. Adaptive neural networks controllers need plant´s Jacobain, but here this problem solved by sliding surface and generalized learning rule in case to eliminate Jacobain problem. The simulation results show that this method is feasible and more effective for magnetic levitation system control.
Keywords :
adaptive control; learning (artificial intelligence); magnetic levitation; neurocontrollers; radial basis function networks; variable structure systems; Jacobain problem; adaptive neural networks controllers; generalized learning rule; magnetic levitation system; radial basis function neural networks; sliding mode control; Adaptive control; Adaptive systems; Coils; Control systems; Jacobian matrices; Magnetic levitation; Neural networks; Programmable control; Radial basis function networks; Sliding mode control; Magnetic levitation system; Radial basis function; Sliding mode; Sliding surface;
Conference_Titel :
Robotics, Automation and Mechatronics, 2008 IEEE Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-1675-2
Electronic_ISBN :
978-1-4244-1676-9
DOI :
10.1109/RAMECH.2008.4681421
