Kriging-Assisted Multi-Objective Design of Permanent Magnet Motor for Position Sensorless Control

Li, Min; Gabriel, Fabien; Alkadri, Maria; Lowther, D.A.

doi:10.1109/tmag.2015.2491301

Cited by 27 publications

(16 citation statements)

References 9 publications

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“…In addition to considering the sampling uniformity, this paper also introduces the gradient information of prediction uncertainty into the sampling strategy. Combining (10)∼ (12) and (19), the derivate of prediction uncertainty can be obtained by…”

Section: B a New Strategy Of Determining The Optimal Samplementioning

confidence: 99%

“…Considering that the surrogate model requires far fewer calls of performance function than other methods, its efficiency is much higher [17]. Kriging model whose theoretical basis is constructed by Matheron is a spatial interpolation technique with minimizing mean square error [18], [19]. It provides the predicted response while giving the corresponding prediction variance.…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Adaptive Reliability Analysis Method Based on Kriging and Weighted Average Misclassification Rate Improvement

et al. 2019

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In the past few years, the Kriging model based on the adaptive design of experiments (DoE) has attracted extensive attention in analyzing the reliability of structures involving time-consuming simulations or complicated implicit performance function. Although varieties of sampling strategies have been proposed, they update DoE mainly by selecting the sample at which its corresponding learning function value is the maximum or minimum. However, there are usually two drawbacks. First, the training samples in DoE are easily clustered or overlapped. Second, the existing strategies usually only consider the improvement of prediction accuracy at the new training sample rather than the accuracy improvement of the region near the new training sample. Unfortunately, these two drawbacks can cause some unnecessary performance function evaluations. Therefore, an efficient adaptive sampling strategy and reliability analysis algorithm based on Kriging model, weighted average misclassification rate, sampling uniformity and gradient of prediction uncertainty are proposed. Furthermore, an improved stopping criterion based on the relative error estimation of failure probability is also developed to further reduce iteration. Subsequently, two explicit examples from the literature are analyzed to verify the effectiveness and superiority of the proposed method. Finally, a truss structure subjected to six external loads is investigated to illustrate the feasibility of the proposed reliability analysis method in engineering applications.

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Section: B a New Strategy Of Determining The Optimal Samplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Efficient Adaptive Reliability Analysis Method Based on Kriging and Weighted Average Misclassification Rate Improvement

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The Kriging surrogate model is a combination of the global approximation model and the local deviation model . The expression is as follows

y (x) = g (x) + z (x)

where x is the design variable, y ( x ) is the fitting function, g ( x ) is the global approximation function of the polynomial response surface, and z ( x ) is the local error function.…”

Section: Kriging Surrogate Modelmentioning

confidence: 99%

“…Several simulation experiments and electromagnetic design have been conducted to verify its reliability . In , the Kriging surrogate model was introduced to multiobjective optimization design. The surrogate model fitted by the Kriging method has global and local statistical characteristics and high fitting precision, which further develops the multiobjective optimization method.…”

Section: Introductionmentioning

confidence: 99%

Optimum design of permanent magnet synchronous generator based on MaxPro sampling and kriging surrogate model

Zeng

Yan

et al. 2019

IEEJ Transactions Elec Engng

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In order to improve the efficiency and quality of a permanent magnet synchronous generator (PMSG) for vehicles, the parametric model and finite-element model of the generator are established. The influence of seven structure parameters on three performance parameters is obtained by simulation analysis, and the sensitivity values of the parameters are given. According to the simulation results, it can be seen that the structure design parameters have higher degrees of freedom, and the relationship among parameters is complex. Using three multidimensional functions as test functions, the precision of the fitting method proposed in this paper is compared with that of the traditional methods. The test results show that, compared with the other methods, maximum projection (MaxPro) sampling and the Kriging method can select as fewer samples as possible while guaranteeing the higher fitting precision. Then, the Kriging surrogate model, which reflects the nonlinear relationship between the structure and performance parameters, is established, and the prediction precision of the model is verified. Based on the surrogate model, the performance of the generator with three performance parameters as optimized objects is optimized by use of elitist nondominated sorting genetic algorithm (NSGA-II), and a Pareto optimal solution set of the objective function is obtained. Three of the optimal solutions are selected for finite-element verification. The results show that the Kriging surrogate model can provide better global and local prediction between input and output variables. It is further proved that the samples obtained by MaxPro sampling can fully reflect the characteristics of the design space. The optimized objects can be weighed according to the optimal decision based on the results of multiobjective optimization through the Kriging surrogate model. The effectiveness of the optimization method is verified. It is of great engineering significance to improve the performance of generators, and the optimal model obtained by this method has an important reference value for prototype trial production.

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“…M ETA-MODELS [1] are used in many fields, mainly to replace expensive black-box models [2] [3]. In an optimization problem the objective function and/or constraints are not always cheaply available data, thus these surrogate models aim to give a model able to approximate the expensive black-box models from a limited number of solutions.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Meta-Model-Based Optimization Under Constraints Using Parallel Computations

et al. 2018

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Meta-models proved to be a very efficient strategy for optimization of expensive black-box models, e.g. Finite Element simulation for electromagnetic devices. It enables to reduce the computational burden for optimization purposes. Kriging is a popular method to build meta-model. Its statistical properties were firstly used in efficient global optimization for unconstrained problems. Afterwards many extensions were introduced in the literature to deal with constrained optimization. This paper presents a comparative study of some infill criteria for constraints handling and a new strategy for parallelization of the expensive computations of models. TEAM workshop problem 22 is taken as an electromagnetic test problem.

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Kriging-Assisted Multi-Objective Design of Permanent Magnet Motor for Position Sensorless Control

Cited by 27 publications

References 9 publications

An Efficient Adaptive Reliability Analysis Method Based on Kriging and Weighted Average Misclassification Rate Improvement

An Efficient Adaptive Reliability Analysis Method Based on Kriging and Weighted Average Misclassification Rate Improvement

Optimum design of permanent magnet synchronous generator based on MaxPro sampling and kriging surrogate model

Enhanced Meta-Model-Based Optimization Under Constraints Using Parallel Computations

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