Abstract
The hybrid electromagnetic and elastic foil gas bearing is explored based on the radial basis function (RBF) neural network in this study so as to improve its stabilization in work. The related principles and structure of hybrid electromagnetic and elastic foil gas bearings is introduced firstly. Then, the proportional, integral, and derivative (PID) bearing controller is introduced and improved into two controllers: IPD and CPID. The controllers and hybrid bearing system are controlled based on the RBF neural network based on deep learning. The characteristics of the hybrid bearing system are explored at the end of this study, and the control simulation research is developed based on the Simulink simulation platform. The effects of the PID, IPD, and CIPD controllers based on the RBF neural network are compared, and they are also compared based on the traditional particle swarm optimization (PSO). The results show that the thickness, spread angle, and rotation speed of the elastic foil have great impacts on the bearing system. The proposed CIPD bearing control method based on RBF neural network has the shortest response time and the best control effect. The controller parameter tuning optimization starts to converge after one generation, which is the fastest iteration. It proves that RBF neural network control based on deep learning has high feasibility in hybrid bearing system. Therefore, the results provide an important reference for the application of deep learning in rotating machinery.
Highlights
The level of industrialization is improving continually, and rotating machinery is developing in a more excellent direction
A hybrid bearing system is obtained by combining the electromagnetic bearing and elastic foil gas bearing
In order to explore the control methods of the hybrid electromagnetic and elastic foil gas bearing system, an radial basis function (RBF) neural network based on the deep learning method is proposed to control the PID controller and the hybrid bearing system
Summary
The level of industrialization is improving continually, and rotating machinery is developing in a more excellent direction. The emergence of electromagnetic bearings and gas bearings has made rotating machinery more and more efficient, and various advantages of these two bearings have made their applications more and more widespread [1,2,3]. Electromagnetic bearing is easy to be controlled, but there are many limitations for its application. Gas bearing has strong adaptability, but it is difficult to be controlled. They are combined in this article to obtain a hybrid bearing system that integrates the advantages of both. There are not many researches on hybrid bearings. Because reliability of the bearing is the first principle of its application, control research on it is essential first of all
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