Title: Robust Q-parametrisation control for nonlinear magnetic bearing systems with imbalance based on TSK fuzzy model

Authors: M. Fekry; Abdelfatah M. Mohamed; Mohammed Fanni

Addresses: Department of Mechatronics and Robotics Engineering, School of Innovative Design Engineering, Egypt-Japan University of Science and Technology (EJUST), Egypt; Department of Electrical Power and Machines Engineering, Zagazig University, Egypt ' Department of Mechatronics and Robotics Engineering, School of Innovative Design Engineering, Egypt-Japan University of Science and Technology (EJUST), Egypt; Department of Electrical and Electronics Engineering, Assuit University, Egypt ' Department of Mechatronics and Robotics Engineering, School of Innovative Design Engineering, Egypt-Japan University of Science and Technology (EJUST), Egypt; Production Engineering & Mechanical Design Department, Mansoura University, Egypt

Abstract: This paper presents a methodology for designing a robust gain scheduled Takagi-Sugeno-Kang (TSK) fuzzy Q-parametrisation controller for nonlinear magnetic bearing systems subjected to imbalance sinusoidal disturbance. First, the mathematical model of nonlinear magnetic bearing is presented. Second, a set of Q-parametrisation observer based stabilising controllers is obtained based on linearisation of the nonlinear system at different operating points. Third, the structure that combines the Q-parametrisation observer based controller (OBC) with TSK fuzzy modelling to overcome the model nonlinearity and expand the operating envelopes is explained. Fourth, the proposed controller is applied to a nonlinear magnetic bearing system. Finally, the simulation results are presented. The results clearly show that the proposed controller is able to merge the intelligence of fuzzy systems with robustness of Q-parametrisation control to extend operating range up to more than 80% of gap length and reject imbalance sinusoidal disturbances at different operating speeds.

Keywords: parallel distribution control; intelligent control; robust control; robust stability; fuzzy supervision; fuzzy modelling; Q-parameterisation; imbalance disturbance rejection; magnetic bearings modelling; magnetic bearings control.

DOI: 10.1504/IJMIC.2018.091237

International Journal of Modelling, Identification and Control, 2018 Vol.29 No.3, pp.195 - 208

Available online: 10 Apr 2018 *

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