Stabilization of three pole Active Magnetic Bearing by sliding mode control techniques

2012 ◽  
Author(s):  
V.A. Sherine Jesna ◽  
S. Ushakumari
Keyword(s):  
Sliding Mode Control ◽  
Sliding Mode ◽  
Magnetic Bearing ◽  
Active Magnetic Bearing ◽  
Control Techniques ◽  
Mode Control

scholarly journals Finite-Time Adaptive Higher-Order SMC for the Nonlinear Five DOF Active Magnetic Bearing System

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1333
Author(s):  
Sudipta Saha ◽  
Syed Muhammad Amrr ◽  
Abdelaziz Salah Saidi ◽  
Arunava Banerjee ◽  
M. Nabi
Keyword(s):  
Sliding Mode Control ◽  
Finite Time ◽  
Sliding Mode ◽  
A Priori ◽  
Magnetic Bearing ◽  
Active Magnetic Bearing ◽  
Mode Control ◽  
Adaptive Laws ◽  
Effective Performance ◽  
Unknown Disturbances

The active magnetic bearings (AMB) play an essential role in supporting the shaft of fast rotating machines and controlling the displacements in the rotors due to the deviation in the shaft. In this paper, an adaptive integral third-order sliding mode control (AITOSMC) is proposed. The controller suppresses the deviations in the rotor and rejects the system uncertainties and unknown disturbances present in the five DOF AMB system. The application of AITOSMC alleviates the problem of high-frequency switching called chattering, which would otherwise restrict the practical application of sliding mode control (SMC). Moreover, adaptive laws are also incorporated in the proposed approach for estimating the controller gains. Further, it also prevents the problem of overestimation and avoids the use of a priori assumption about the upper bound knowledge of total disturbance. The Lyapunov and homogeneity theories are exploited for the stability proof, which guarantees the finite-time convergence of closed-loop and output signals. The numerical analysis of the proposed strategy illustrates the effective performance. Furthermore, the comparative analysis with the existing control schemes demonstrates the efficacy of the proposed controller.

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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