Inverse Compensator for A Simplified Discrete Preisach Model Using Model-Order Reduction Approach

Li, Zhi; Shan, Jinjun; Gabbert, Ulrich

doi:10.1109/tie.2018.2873111

Cited by 41 publications

(14 citation statements)

References 25 publications

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“…In (15), all the parameters are treated with the same weight. In practice, different parameters may have different effects on the results.…”

Section: Identification Of Functions Of Capacitancementioning

confidence: 99%

Modeling and Identification of Temperature-Dependent Hysteresis in Piezoelectric Materials Considering Parameter Sensitivity

Liu

2020

IEEE Access

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Hysteresis is an important nonlinearity effect in piezoelectric materials and is sensitive to temperature. This paper extends the saturated capacitor model to capture temperature-dependent hysteresis by replacing the constant parameters with functions of temperature. To obtain these functions, several sets of constant parameters are identified at different temperatures, and then each term of the parameters is fitted as a function with respect to temperature. In the curve fitting process, the parameter sensitivity is employed as a weight factor. The effectiveness of the proposed modeling and parameter determination procedure is verified with experimental data. The overall normalized root mean square error is approximately 5%, including 3% error caused by the identified parameters. INDEX TERMS Temperature-dependent hysteresis, modeling and identification, piezoelectric materials, saturated capacitor hysteresis model.

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“…In (15), all the parameters are treated with the same weight. In practice, different parameters may have different effects on the results.…”

Section: Identification Of Functions Of Capacitancementioning

confidence: 99%

Modeling and Identification of Temperature-Dependent Hysteresis in Piezoelectric Materials Considering Parameter Sensitivity

Liu

2020

IEEE Access

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“…The celebrated active-set method in [24] is widely used to solve (5). For example, earlier work [20]- [22] utilized the MATLAB function lsqnonneg that implements this very method. Before we proceed, we illustrate that the nonegativity constraint itself may yield misleading interpretations of Preisach density shape, although it results in satisfactory fittings of hysteresis.…”

Section: Subset Selection Model For Estimating Preisach Densitymentioning

confidence: 99%

“…Model consisting of larger number of relay operators has less hysteresis reconstruction error but at higher computational cost [6], [20]. Li et al [21], [22] utilized discrete empirical interpolation method (DEIM) [23] to reduce the number of relay operators without losing accuracy of reconstructing hysteresis, leading to successful applications on actuator control tasks.…”

Section: Introductionmentioning

confidence: 99%

“…Earlier work [20]- [22] applied the active-set nonnegative least square (NNLS) [24] algorithm to identify weights. For applications relying on reconstructing hysteresis such as those in [20]- [22], the active-set NNLS algorithm [24] is a perfect choice due to its availability in software platforms and easiness to use (free of parameter-tuning). However, for cases involving interpretation of PD shape, we show that the nonnegativity constraint itself may not be enough due to the collinearity issue.…”

Section: Introductionmentioning

confidence: 99%

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Estimating Preisach Density via Subset Selection

Kim

Neumayer

et al. 2020

IEEE Access

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Preisach density is drawing increasing attention for interpreting material properties for memory and storage electronics. Preisach density can be linked to the observed hysteresis loops via the Preisach model that is based on the superposition of relay operators. Reconstructing Preisach density from hysteresis is an ill-posed problem with nonunique solutions. To alleviate ambiguities, we address Preisach density reconstruction as a constrained subset selection task utilizing structured sparsity regularizations. We validate our approach under various simulation settings and apply it on experimental band-excitation piezoresponse spectroscopy (BEPS) datasets to gain insights in microstructure-dependent properties of the tip-surface contact.

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“…MOR has been conventionally applied to large-scale-systems [30][31][32][33]. Its application in the power electronics domain seems rather counter-intuitive since these systems are of considerably smaller size.…”

Section: Introductionmentioning

confidence: 99%

Adaptive multi‐resolution framework for fast simulation of power electronic circuits

Khan

Bazaz

Nahvi

2020

IET Circuits, Devices & Systems

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An adaptive multi-resolution simulation (AMRS) framework for fast and accurate simulation of high-fidelity models of power electronic circuits (PECs) is presented in this study. The wide span of eigenvalues makes PEC simulation prohibitively slow. This problem can be tackled using the proposed approach. Singular perturbation approximation is used to extract simplified models by ignoring the transient contribution of the non-dominant eigenvalues. Simplified models of different orders or resolutions, each corresponding to a particular level of accuracy are derived on the fly at no additional computational cost. The simulation engine is set such that it adaptively switches across resolutions based on a predefined tolerance during the simulation. This approach is advantageous in the sense that instead of simulating the original model over the entire time-range, a combination of the original and simplified models is simulated. The combined use of the original and the simplified models is thus shown to be a powerful tool for efficient and accurate simulation of PECs. The examples illustrated, show that the AMRS approach makes the simulation considerably faster and ensures the complete response of the system is obtained with negligible error. The method is illustrated on a Class-E amplifier and a DC-DC buck-boost converter.

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Inverse Compensator for A Simplified Discrete Preisach Model Using Model-Order Reduction Approach

Cited by 41 publications

References 25 publications

Modeling and Identification of Temperature-Dependent Hysteresis in Piezoelectric Materials Considering Parameter Sensitivity

Modeling and Identification of Temperature-Dependent Hysteresis in Piezoelectric Materials Considering Parameter Sensitivity

Estimating Preisach Density via Subset Selection

Adaptive multi‐resolution framework for fast simulation of power electronic circuits

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