ANFIS controller for an Active Magnetic Bearing system

Chen, Seng-Chi; Nguyen, Van-Sum; Le, Dinh-Kha; Hsu, Ming-Mao

doi:10.1109/fuzz-ieee.2013.6622360

Cited by 11 publications

(5 citation statements)

References 13 publications

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“…The variable is the bias current and and are control currents along the -and -axes, respectively; 1 and 1 are the rotor displacements at the magnetic bearing. Following Schweitzer [16][17][18], the total nonlinear attractive electromagnetic forces along the -and -axes are given as follows:…”

Section: Structure and Mathematical Model Of The Active Magnetic Bearmentioning

confidence: 99%

Nonlinear Control of an Active Magnetic Bearing System Achieved Using a Fuzzy Control with Radial Basis Function Neural Network

Chen

Nguyen

et al. 2014

Journal of Applied Mathematics

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Studies on active magnetic bearing (AMB) systems are increasing in popularity and practical applications. Magnetic bearings cause less noise, friction, and vibration than the conventional mechanical bearings; however, the control of AMB systems requires further investigation. The magnetic force has a highly nonlinear relation to the control current and the air gap. This paper proposes an intelligent control method for positioning an AMB system that uses a neural fuzzy controller (NFC). The mathematical model of an AMB system comprises identification followed by collection of information from this system. A fuzzy logic controller (FLC), the parameters of which are adjusted using a radial basis function neural network (RBFNN), is applied to the unbalanced vibration in an AMB system. The AMB system exhibited a satisfactory control performance, with low overshoot, and produced improved transient and steady-state responses under various operating conditions. The NFC has been verified on a prototype AMB system. The proposed controller can be feasibly applied to AMB systems exposed to various external disturbances; demonstrating the effectiveness of the NFC with self-learning and self-improving capacities is proven.

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Section: Structure and Mathematical Model Of The Active Magnetic Bearmentioning

confidence: 99%

Nonlinear Control of an Active Magnetic Bearing System Achieved Using a Fuzzy Control with Radial Basis Function Neural Network

Chen

Nguyen

et al. 2014

Journal of Applied Mathematics

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“…Fuzzy logic control (FLC) has been used in many areas and widely chosen to balance the magnetic bearing system. As magnetic bearing system are highly non-linear and inherently unstable, non-linear fuzzy logic controller is designed for system stabilization [11][12][13]. The fuzzy logic control algorithm is chosen to balance the system and to enhance the performance in this study.…”

Section: Fuzzy Logic Control Of Magnetic Bearing Systemmentioning

confidence: 99%

Design and control of an 8-slot radial flux magnetic bearing

Güleç

Yolaçan

Aydın

2014

2014 International Symposium on Power Electronics, Electrical Drives, Automation and Motion

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This work presents the design and control of an 8slot radial flux magnetic bearing. Design of magnetic bearing is carried out with coupled magnetic equivalent circuit model and finite element analysis (FEA). The magnetic design is verified with 2D FEA and good agreement between the two is obtained. Designed magnetic bearing is assembled into a system and fuzzy logic control is applied to the magnetic bearings to adjust the currents in order to balance the system shaft. The control is performed at the equilibrium position under different disturbances and detailed information about the working space of the designed magnetic bearings is provided.

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“…[4] proposed a real-time adaptive feed-forward compensation controller based on variable-step least mean square algorithm to compensate the unbalance response and reduce the fluctuation of the AMB system. [5] considered the high nonlinearity between the air gap, current and the electromagnetic force, and presented an approach based fuzzy control and adaptive neuro-fuzzy inference methods. [6] presented a new structure of sensorless control with three-pole active magnetic bearings.…”

Section: Introductionmentioning

confidence: 99%

“…[6] presented a new structure of sensorless control with three-pole active magnetic bearings. Now, the most common method to produce levitation force in AMB is through electromagnets [1][2][3][4][5][6] . However, as previously mentioned, the nonlinearity between air gap, exciting current and the levitation force makes the controller design difficult and complex.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Control Approach of an Electromagnetic Bearing System

Liu

Yang

et al. 2014

AMM

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This paper proposes a two-electromagnet double degrees of freedom active magnetic bearing (AMB) model. Considering the nonlinearity between the electromagnetic force and the air gap, the authors put forward a nonlinear control approach based on feedback linearization which decouples and linearizes the original system. For the linearized system, pole placement strategy is used to achieve expected steady and dynamic performances. Simulation results show that this approach can successfully implement the decoupling and linearization, and is effective of different initial values.

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ANFIS controller for an Active Magnetic Bearing system

Cited by 11 publications

References 13 publications

Nonlinear Control of an Active Magnetic Bearing System Achieved Using a Fuzzy Control with Radial Basis Function Neural Network

Nonlinear Control of an Active Magnetic Bearing System Achieved Using a Fuzzy Control with Radial Basis Function Neural Network

Design and control of an 8-slot radial flux magnetic bearing

Nonlinear Control Approach of an Electromagnetic Bearing System

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