Advanced Magnetic Bearing Device for High-Speed Applications with an I-type Electromagnet

Debnath, Sukanta; Biswas⃰, Pabitra Kumar

doi:10.1080/15325008.2021.1908454

Cited by 6 publications

(6 citation statements)

References 13 publications

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“…A copper coil with a relative permeability of 0.88 is coiled around an iron stator with a relative permeability of 11,000. Additionally, the rotor is composed of iron with a relative permeability of 5000 [27,28]. Mass of the rotor (m) is 0.654 Kg, the copper coil has a resistance (R) of 1.2 Ω and at nominal point of operation the inductance (L) is 0.020598 H. The hardware model shown in Figure 2 as a closed-loop representation is for simulation purpose.…”

Section: Modelling and Designing Of Active Magnetic Bearing Systemmentioning

confidence: 99%

“…A most acceptable technique to design and analyse a controller for AMBs is to use linear approximations [31] of the ). In form of transfer function the proposed AMB system is represented as [27],…”

Section: Modelling and Designing Of Active Magnetic Bearing Systemmentioning

confidence: 99%

“…A most acceptable technique to design and analyse a controller for AMBs is to use linear approximations [31] of the non‐linear model. The plant model is approximately linearized [32] at an equilibrium point

( {{i_o},{x_o}} ).

In form of transfer function the proposed AMB system is represented as [27],

\begin{equation}\frac{{X( s )}}{{I( s )}} = - \frac{{{K_a}}}{{m{s^2} - {K_z}}}\end{equation}

…”

Section: Modelling and Designing Of Active Magnetic Bearing Systemmentioning

confidence: 99%

“…Giap et al, (2020) utilized Mamdani FIS to create a fuzzy sliding mode control (FSMC) to minimise the disturbance effect on AMB system [18]. Other than fuzzy logic-based control some other control strategies [19][20][21][22][23][24][25][26][27] are listed in Table 1.…”

Section: Introductionmentioning

confidence: 99%

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Modelling, simulation and performance comparison of different membership functions based fuzzy logic control for an active magnetic bearing system

Gupta

Biswas⃰

Aljafari

et al. 2023

The Journal of Engineering

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In-house prototype model of an active magnetic bearing is built and using its physical parameter values a linearized transfer function of order two, is mathematically obtained for a specific point of operation. In this work, a closed loop active magnetic bearing system is proposed and controller for this closed loop is designed using fuzzy logic control. Two different fuzzy inference systems: Mamdani and Sugeno fuzzy inference system constructed individually with eleven different types of membership functions separately. The considered membership functions are-linear, generalized bell-shaped, Gaussian distribution based, quadratic, cubic polynomial and sigmoidal curve-based membership functions. Calculating the transient state and steady state parameter values together with the controller output allows to see how these various membership functions perform on the proposed closed loop active magnetic bearing system. Observed data shows that changing the fuzzy inference system for a specific membership function will result in 67.41% reduction in the overshoot value, 29.70% reduction in settling time value, 10.42% change in rise time value and 2.91% variation is peak time value. Practicality of all membership functions are analysed based on calculated data and their performances with the proposed model.

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Section: Modelling and Designing Of Active Magnetic Bearing Systemmentioning

confidence: 99%

Section: Modelling and Designing Of Active Magnetic Bearing Systemmentioning

confidence: 99%

( {{i_o},{x_o}} ).

In form of transfer function the proposed AMB system is represented as [27],

\begin{equation}\frac{{X( s )}}{{I( s )}} = - \frac{{{K_a}}}{{m{s^2} - {K_z}}}\end{equation}

…”

Section: Modelling and Designing Of Active Magnetic Bearing Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modelling, simulation and performance comparison of different membership functions based fuzzy logic control for an active magnetic bearing system

Gupta

Biswas⃰

Aljafari

et al. 2023

The Journal of Engineering

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“…Nonlinearities and dynamic uncertainties of AMB systems make them complex to analyze and design. Therefore, for a nominal point (i o , x o ) of operation, the proposed system is linearized [30], [31] and can be written in a transfer function form as depicted in Equation 1, [32],…”

Section: Proposed In-house Model Of Amb Systemmentioning

confidence: 99%

Metaheuristic Optimization Techniques Used in Controlling of an Active Magnetic Bearing System for High-Speed Machining Application

et al. 2023

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Smart control tactics, wider stability region, rapid reaction time, and high-speed performance are essential requirements for any controller to provide a smooth, vibrationless, and efficient performance of an in-house fabricated active magnetic bearing (AMB) system. In this manuscript, three pre-eminent population-based metaheuristic optimization techniques: Genetic algorithm (GA), Particle swarm optimization (PSO), and Cuckoo search algorithm (CSA) are implemented one by one, to calculate optimized gain parameters of PID controller for the proposed closed-loop active magnetic bearing (AMB) system. Performance indices or, objective functions on which these optimization techniques are executed are integral absolute error (IAE), integral square error (ISE), integral time multiplied absolute error (ITAE), and integral time multiplied square error (ITSE). The significance of an optimization technique and objective function can obtain only by implementing it. As a result, several comparisons are made based on statistical performance, time domain, frequency response behavior, and algorithm execution time. Finally, the applicability of optimization strategies in addition to the performance indices is determined with the aid of the comparative analysis. That could assist in choosing a suitable optimization technique along with a performance index for a high-speed application of an active magnetic bearing system.INDEX TERMS Active magnetic bearing, genetic algorithm, cuckoo search algorithm, particle swarm optimization.

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Hunting Based Optimization Techniques Used in Controlling an Active Magnetic Bearing System

Gupta,

Biswas,

Babu

et al. 2022

IEEE Access

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Advanced Magnetic Bearing Device for High-Speed Applications with an I-type Electromagnet

Cited by 6 publications

References 13 publications

Modelling, simulation and performance comparison of different membership functions based fuzzy logic control for an active magnetic bearing system

Modelling, simulation and performance comparison of different membership functions based fuzzy logic control for an active magnetic bearing system

Metaheuristic Optimization Techniques Used in Controlling of an Active Magnetic Bearing System for High-Speed Machining Application

Hunting Based Optimization Techniques Used in Controlling an Active Magnetic Bearing System

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