Modeling and nonlinear control of magnetic levitation systems

Hajjaji, A. El; Ouladsine, Mustapha

doi:10.1109/41.937416

Cited by 252 publications

(105 citation statements)

References 10 publications

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“…Therefore, applications of the feedback linearization control techniques have been presented in many studies for the magnetic suspension systems [5]- [8]. In [5] and [6], feedback linearization techniques were adopted to transform the system model into an equivalent model with a simple form. Although the dynamic model of the magnetic suspension system was simplified, only the nominal system parameters were considered in the feedback linearization design.…”

Section: Introductionmentioning

confidence: 99%

Control of the Magnetic Suspension System with a Three-degree-of-freedom Using RBF Neural Network Controller

Saberi¹,

Altafi²,

Alizadeh³

2012

IJCEE

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Abstract-In this paper an intelligent method is proposed for controlling a kind of magnetic suspension system with 3 degree of freedom. At first, the dynamic of the magnetic suspension system and the related equations are presented. Regarding unstable nature and non-linearity of magnetic suspension system using techniques of liner control for achieving optimal performance so that all requirements of system are met in all domains is difficult. Then optimal controlling input for magnetic suspension system is designed using optimal control method, linear quadratic regulator (LQR) and required computations. For designing the neural network controller, Radial Basis Function (RBF), we use the results gained by LQR controller. The simulation results are performed using MATLAB software and performance of proposed controlling method was approved.

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Section: Introductionmentioning

confidence: 99%

Control of the Magnetic Suspension System with a Three-degree-of-freedom Using RBF Neural Network Controller

Saberi¹,

Altafi²,

Alizadeh³

2012

IJCEE

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“…이러한 특성 때문에 슬라이딩제어, 피드백 선형화, 백 스텝핑 제어 등 다양한 모델기반 제어기법이 적용되었다 [7][8][9][10][11]. [6,8,11,13,14] 부상체가 영구자석일 경우에는 자기력이 전자석에 흐르는 전류에 비례한다고 가 정하고 전자석을 모델링 하였다 [15]. 또는 모델식의 차수를 줄이기 위하여 자기력이 전류에 비례한다고 가정하지 않고 전압의 크기에 비례한다고 가정하는 경우도 있다 [7] …”

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Development of Magnetic Force Modeling Equipment for Magnetic Levitation Systems

Yang¹,

Kim²,

Lee³

et al. 2011

Journal of Institute of Control, Robotics and Systems

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This paper proposes an equipment and an algorithm for modeling the magnetic force of electromagnets in magnetic levitation systems. We assume that the magnetic force model is represented in terms of a 2D lookup table. The 2D lookup table is constructed by applying noncausal filtering and interpolation to data measured by the proposed modeling equipment. The proposed modeling equipment is designed such that it can measure the magnetic force exerted on the levitation object while it changes the voltage applied to the electromagnet and position of the levitation object. The algorithm of making a 2D lookup table has two stages. The data measured by the proposed modeling equipment is smoothed by a noncausal filter and then the 2D lookup table is obtained by interpolating filtered data. The proposed modeling method has advantages of time-saving, model consistency, and chance of automation for mass production. We show the validity of proposed method through control experiments.

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“…M AGNETIC levitation systems have been successfully implemented for many applications, such as frictionless bearings, high-speed maglev passenger trains, and planar positioning systems [1]- [5]. Due to the features of the open-loop instability and inherent nonlinearities in electromechanical dynamics of the magnetic levitation apparatuses, the development of the high-performance control design for the position control of the levitated object is very important.…”

Section: Introductionmentioning

confidence: 99%

“…In general, the electromechanical dynamics of the magnetic levitation apparatuses is represented by a nonlinear model consisting of the state variables of position, velocity, and coil current signals. Therefore, applications of the feedback linearization control techniques have been presented in many studies [5]- [9]. In [5] and [6], the feedback linearization technique through transforming the system model into an equivalent model with simpler form.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, applications of the feedback linearization control techniques have been presented in many studies [5]- [9]. In [5] and [6], the feedback linearization technique through transforming the system model into an equivalent model with simpler form. Although the dynamic model of the magnetic levitation apparatus was simplified, only the constant system parameters were considered in feedback linearization design.…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Adaptive Backstepping Control System for Magnetic Levitation Apparatus

2007

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We propose an intelligent adaptive backstepping control system using a recurrent neural network (RNN) to control the mover position of a magnetic levitation apparatus to compensate for uncertainties, including friction force. First, we derive a dynamic model of the magnetic levitation apparatus. Then, we suggest an adaptive backstepping approach to compensate disturbances, including the friction force, occurring in the motion control system. To further increase the robustness of the magnetic levitation apparatus, we propose an RNN estimator for the required lumped uncertainty in the adaptive backstepping control system. We further propose an online parameter training methodology, derived by the gradient descent method, to increase the learning capability of the RNN. The effectiveness of the proposed control scheme has been verified by experiment. With the proposed adaptive backstepping control system using RNN, the mover position of the magnetic levitation apparatus possesses the advantages of good transient control performance and robustness to uncertainties for the tracking of periodic trajectories.Index Terms-Adaptive backstepping control, lumped uncertainty, magnetic levitation, recurrent neural network.

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Modeling and nonlinear control of magnetic levitation systems

Cited by 252 publications

References 10 publications

Control of the Magnetic Suspension System with a Three-degree-of-freedom Using RBF Neural Network Controller

Control of the Magnetic Suspension System with a Three-degree-of-freedom Using RBF Neural Network Controller

Development of Magnetic Force Modeling Equipment for Magnetic Levitation Systems

Intelligent Adaptive Backstepping Control System for Magnetic Levitation Apparatus

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