A RBFNN-Based Adaptive Disturbance Compensation Approach Applied to Magnetic Suspension Inertially Stabilized Platform. (3rd August 2014)

• Record Type: Journal Article
• Authors: Mu, Quanqi; Liu, Gang; Lei, Xusheng
• Other Names: Chen Yi Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2014(2014)
• Publication Date: 2014-08-03
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2014/657985
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : 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.