Optimization of Jiles-Atherton Hysteresis Model Parameters Using Taguchi’s Method

Zaman, Mohammad Asif; Matin, Md. Abdul

doi:10.1109/tmag.2014.2357394

Cited by 30 publications

(19 citation statements)

References 15 publications

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“…Since y d is bounded, considering (18) and (19), the error x is bounded, that is, x −x ≤ χ, where χ is a positive constant. Then, we define ρ 1 = | Λ T 1 x| ≤ max Λ i , 1 χ and ρ 2 = | 0Λ T x| ≤ max Λ i χ as small positive constants.…”

Section: Theoremmentioning

confidence: 99%

“…In the simulation, the tracking error is defined in (18) and Λ is adopted as Λ = 1, 5, 6 T . The controller (21) …”

Section: Simulationsmentioning

confidence: 99%

“…Plenty of results are obtained, and the hysteresis model can be roughly divided into two classes: the physical-based model and the mathematicalbased model. The typical hysteresis physical-based model is the Jiles-Atherton (J-A) model [16][17][18]. The physical model is constructed with the physical parameters of the hysteresis systems.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive Neural Control for Hysteresis Motor Driving Servo System with Bouc‐Wen Model

Gao

2018

Complexity

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An adaptive high-order neural network (HONN) control strategy is proposed for a hysteresis motor driving servo system with the Bouc-Wen model. To simplify control design, the model is rewritten as a canonical state space form firstly through coordinate transformation. Then, a high-gain state observer (HGSO) is proposed to estimate the unknown transformed state. Afterward, a filter for the tracking errors is adopted which converts the vector error e into a scalar error s. Finally, an adaptive HONN controller is presented, and a Lyapunov function candidate guarantees that all the closed-loop signals are uniformly ultimately bounded (UUB). Simulations verified the effectiveness of the proposed neural network adaptive control strategy for the hysteresis servo motor system.

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Section: Theoremmentioning

confidence: 99%

“…In the simulation, the tracking error is defined in (18) and Λ is adopted as Λ = 1, 5, 6 T . The controller (21) …”

Section: Simulationsmentioning

confidence: 99%

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Adaptive Neural Control for Hysteresis Motor Driving Servo System with Bouc‐Wen Model

Gao

2018

Complexity

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“…CALKINS firstly applied the quadratic programming algorithm and least square method to identify model parameters. 9 With the development of the intelligent identification algorithm, Adaptive genetic algorithm, 10 Particle swarm algorithm, 11 Neural network algorithm 12 and Taguchi method 13 are respectively studied and adopted to achieve favorable results. However, how to determine the initial range of unknown parameters, enhance the convergence speed and precision and avoid falling into local optimal solutions becomes the common problem for identification algorithm.…”

Section: Introductionmentioning

confidence: 99%

Parameter identification based on modified simulated annealing differential evolution algorithm for giant magnetostrictive actuator

Gao

Liu

2018

AIP Advances

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There is a serious nonlinear relationship between input and output in the giant magnetostrictive actuator (GMA) and how to establish mathematical model and identify its parameters is very important to study characteristics and improve control accuracy. The current-displacement model is firstly built based on Jiles-Atherton (J-A) model theory, Ampere loop theorem and stress-magnetism coupling model. And then laws between unknown parameters and hysteresis loops are studied to determine the data-taking scope. The modified simulated annealing differential evolution algorithm (MSADEA) is proposed by taking full advantage of differential evolution algorithm’s fast convergence and simulated annealing algorithm’s jumping property to enhance the convergence speed and performance. Simulation and experiment results shows that this algorithm is not only simple and efficient, but also has fast convergence speed and high identification accuracy.

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“…Note that the identification of the hysteresis models is widely discussed in the literature, eg, in Chwastek and Zaman and Matin for JA parameters or in Zaman and Sikder for identification of the Bouc‐Wen hysteresis model. The methods for the identification of the JA parameters are not discussed in the following of this paper.…”

Section: Introductionmentioning

confidence: 99%

Using a Jiles‐Atherton vector hysteresis model for isotropic magnetic materials with the finite element method, Newton‐Raphson method, and relaxation procedure

Guérin

Jacques

Sabariego

et al. 2016

Int J Numerical Modelling

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This paper deals with the use of a Jiles‐Atherton vector hysteresis model included in 2D finite element modeling. The hysteresis model is only valid for isotropic materials. It is implemented with the vector potential formulation in 2D along with electric circuit equations to account for a possible external circuit. The Newton‐Raphson algorithm is used with a relaxation procedure, whereby at each iteration, the relaxation coefficient is sought so as to minimize the Euclidean norm of the residual of the finite element nonlinear system of equations. We have simulated several numerical examples with the proposed approach. First, simulations on a square domain were conducted so as to validate the model. We have further simulated a T‐shaped magnetic circuit (exhibiting rotating flux) and a 3‐phase 3‐limb transformer model. For these 2 cases, the eddy current losses in the laminations are taken into account by a low‐frequency model. We have finally performed simulations on the TEAM workshop problem 32, which consists of a 3‐limb transformer with 2 windings, for which current and local magnetic flux density measurements are available. We obtained a good agreement between computed and measured results.

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Optimization of Jiles-Atherton Hysteresis Model Parameters Using Taguchi’s Method

Cited by 30 publications

References 15 publications

Adaptive Neural Control for Hysteresis Motor Driving Servo System with Bouc‐Wen Model

Adaptive Neural Control for Hysteresis Motor Driving Servo System with Bouc‐Wen Model

Parameter identification based on modified simulated annealing differential evolution algorithm for giant magnetostrictive actuator

Using a Jiles‐Atherton vector hysteresis model for isotropic magnetic materials with the finite element method, Newton‐Raphson method, and relaxation procedure

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