Sliding Mode Control with Neural Network for Active Magnetic Bearing System

2019 ◽  
Author(s):  
Zhi Cao ◽  
Jianning Dong ◽  
Faisal Wani ◽  
Henk Polinder ◽  
Pavol Bauer ◽  
...  
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Magnetic Bearing ◽  
Active Magnetic Bearing ◽  
Mode Control ◽  
Bearing System

Integral Sliding Mode Control Approach for 3-Pole Active Magnetic Bearing System

2016 ◽  
Vol 829 ◽  
pp. 128-132 ◽  
Author(s):  
Van Van Huynh ◽  
Minh Hoang Quang Tran
Keyword(s):  
Sliding Mode Control ◽  
Sliding Mode ◽  
Magnetic Bearing ◽  
Active Magnetic Bearing ◽  
Sliding Surface ◽  
Integral Sliding Mode Control ◽  
Integral Sliding Mode ◽  
Mode Control ◽  
Integral Sliding Surface ◽  
Bearing System

In this paper, a new integral sliding mode control scheme is designed for the 3-pole active magnetic bearing system. First, a new integral sliding surface is designed such that the 3-pole active magnetic bearing system in the sliding mode is asymptotically stable under certain conditions. Then, an adaptive controller is designed to solve the unknown upper bound of matched uncertainty and guarantee the reachability of the integral sliding surface. Finally, the performance of the proposed integral sliding mode controller is applied to 3-pole active magnetic bearing system to demonstrate the efficacy of the proposed method.

Sliding mode control with deep learning method for rotor trajectory control of active magnetic bearing system

2018 ◽  
Vol 41 (5) ◽  
pp. 1383-1394 ◽  
Author(s):  
Xuan Yao ◽  
Zhaobo Chen
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sliding Mode Control ◽  
Convolutional Neural Network ◽  
Sliding Mode ◽  
Magnetic Bearing ◽  
Trajectory Control ◽  
Active Magnetic Bearing ◽  
Learning Method ◽  
Mode Control

Active magnetic bearing (AMB) is competent in rotor trajectory control for potential applications such as mechanical processing and spindle attitude control, while the highly nonlinear and coupled dynamic characteristics especially in the condition of rotor large motion are obstacles in controller design. In this paper, a controller of AMB is proposed to achieve rotor 3D trajectory control. First, the dynamic model of the AMB-rotor system containing a nonlinear electromagnetic force model is introduced. Then the DCNN-SMC (deep convolutional neural network - sliding mode control) controller is proposed. Sliding mode control is used to achieve the tracking control with high robustness and responsiveness, and a deep convolutional neural network based on deep learning method is designed to compensate the uncertainties of the system. Finally, simulation of a 5-degree of freedom (DOF) system on various trajectories demonstrates evident control effect of the proposed controller in precision and significant effect of DCNN based on deep learning method in compensation control.

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