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Mathematical Problems in Engineering
Volume 2014, Article ID 657985, 9 pages
Research Article

A RBFNN-Based Adaptive Disturbance Compensation Approach Applied to Magnetic Suspension Inertially Stabilized Platform

Quanqi Mu,1,2 Gang Liu,1,2 and Xusheng Lei1,2

1Science and Technology on Inertial Laboratory, Beihang University, Beijing 100191, China
2School of Instrument Science and Opto-Eletronics Engineering, Beihang University, Beijing 100191, China

Received 5 March 2014; Revised 11 June 2014; Accepted 11 June 2014; Published 3 August 2014

Academic Editor: Yi Chen

Copyright © 2014 Quanqi Mu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Compared with traditional mechanical inertially stabilized platform (ISP), magnetic suspension ISP (MSISP) can absorb high frequency vibrations via a magnetic suspension bearing system with five degrees of freedom between azimuth and pitch gimbals. However, force acting between rotor and stator will introduce coupling torque to roll and pitch gimbals. Since the disturbance of magnetic bearings has strong nonlinearity, classic state feedback control algorithm cannot bring higher precision control for MSISP. In order to enhance the control accuracy for MSISP, a disturbance compensator based on radial basis function neural network (RBFNN) is developed to compensate for the disturbance. Using the Lyapunov theorem, the weighting matrix of RBFNN can be updated online. Therefore, the RBFNN can be constructed without priori training. At last, simulations and experiment results validate that the compensation method proposed in this paper can improve ISP accuracy significantly.