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Abstract

The paper presents application of a modified, symmetrical Bouc-Wen model to simulate the mechanical behaviour of high-frequency piezoelectric actuators (PAs). In order to identify parameters of the model, a two-step algorithm was developed. In its first stage, the mechanical parameters were identified by taking into account their bilinear variability and using a square input voltage waveform. In the second step, the hysteresis parameters were determined based on a periodic excitation. Additionally, in order to reduce the influence of measurement errors in determination of selected derivatives the continuum wavelet transform (CWT) and translation-rotation transformation (TRT) methods were applied. The results proved that the modified symmetrical Bouc-Wen model is able to describe the mechanical behaviour of PAs across a wide frequency range.

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Authors and Affiliations

Rafał Kędra
Magdalena Rucka
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Abstract

Today, a cascaded system of position loop, velocity loop and current loop is standard in industrial motion controllers. The exact knowledge of significant parameters in the loops is the basis for the tuning of the servo controllers. A new method to support the commissioning has been developed. It enables the user to identify the moment of inertia as well as the time constant of the closed current loop simultaneously. The method is based on the auto relay feedback experiment by Aström and Hägglund. The model parameters are automatically adjusted according to the time behaviour of the controlled system. For this purpose, the auto relay feedback experiment is combined with the technique of gradual pole compensation. In comparison to other existing methods, this approach has the advantage that a parametric model for the open velocity loop is derived directly.

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Authors and Affiliations

Reimund Neugebauer
Stefan Hofmann
Arvid Hellmich
Holger Schlegel
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Abstract

The paper presents an adapted least squares identification method for reduced-order parametric models. On the example of the open velocity loop, different model approaches were implemented in a motion control system. Furthermore, it is demonstrated how the accuracy of the method can be improved. Finally, experimental results are shown.

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Authors and Affiliations

Reimund Neugebauer
Arvid Hellmich
Stefan Hofmann
Holger Schlegel
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Abstract

For reasons of reliability, stability, safety and economy, controlling and monitoring the response of structures during the time of use, either permanently or temporally, is of increasing importance. Experimental methods enable in-situ measuring deformations of any kind of structures and enable drawing conclusions over the actual state of the structures. However, to obtain reliable knowledge of the real internal conditions like the strength of materials and the actual stress-state, as well as of their changes over time, caused by ageing, fatigue and environmental influences, always an inverse problem must be solved. That requires special mathematical algorithms. Especially for time-depending material response it might be quite important to know the material parameters at any time and furthermore the internal stress-state also. Therefore, a method will be presented to solve the inverse problem of parameter identification with reference to linear visco-elastic materials.
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Authors and Affiliations

Karl-Hans Laerrnann
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Bibliography

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Authors and Affiliations

Lin Sun
1
Jing Song
2
Yan Jin
1

  1. Wuchang University of Technology, China
  2. National University of Defense Technology, China
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Abstract

A novel magnetically-coupled energy storage inductor boost inverter circuit for renewable energy and the dual-mode control strategy with instantaneous value feedback of output voltage are proposed. In-depth research and analysis on the circuit, control strategy, voltage transmission characteristics, etc., providing the parameter design method of magnetically-coupled energy storage inductors and output filter. The circuit topology is cascaded by the input source ��in, the input filter ��in, a single-phase inverter bridge with a magnetically-coupled energy storage inductor, and a CL filter; The control strategy serves the output voltage as a reference to achieve the switch of step-down and step-up modes smoothly. The simulation results of a 1000 VA 100–200 VDC, 220 V 50 Hz AC inverter show that the proposed inverter can realize single-stage boost power conversion, which can adapt to resistive, capacitive and inductive loads, has high power density and low output waveform distortion. It has good application prospects in small and medium-capacity single-phase inverter occasions with low input voltage.
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Bibliography

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Authors and Affiliations

Yiwen Chen
1
Sixu Luo
1
ORCID: ORCID
Zhiliang Huang
2
Jiahui Jiang
3
ORCID: ORCID

  1. Fujian Key Laboratory of New Energy Generation and Power Conversion, Fuzhou University, China
  2. Texas Instruments Semiconductor Technologies (Shanghai) Co., Ltd., China
  3. College of Electrical Engineering, Qingdao University, China
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Abstract

Temperature rise of the hub motor in distributed drive electric vehicles (DDEVs) under long-time and overload operating conditions brings parameter drift and degrades the performance of the motor. A novel online parameter identification method based on improved teaching-learning-based optimization (ITLBO) is proposed to estimate the stator resistance, ��-axis inductance, ��-axis inductance, and flux linkage of the hub motor with respect to temperature rise. The effect of temperature rise on the stator resistance, ��-axis inductance, ��-axis inductance, and magnetic flux linkage is analysed. The hub motor parameters are identified offline. The proposed ITLBO algorithm is introduced to estimate the parameters online. The Gaussian perturbation function is employed to optimize the TLBO algorithm and improve the identification speed and accuracy. The mechanisms of group learning and low-ranking elimination are established. After that, the proposed ITLBO algorithm for parameter identification is employed to identify the hub motor parameters online on the test bench. Compared with other parameter identification algorithms, both simulation and experimental results show the proposed ITLBO algorithm has rapid convergence and a higher convergence precision, by which the robustness of the algorithm is effectively verified. Keywords: parameters identification, teaching–learning-based optimization, hub motor, temperature rise.
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Authors and Affiliations

Yong Li
1
Juan Wang
2
Taohua Zhang
2
Han Hu
1
Hao Wu
1

  1. Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China
  2. Beijing Institute of Space Launch Technology, Beijing 100076, China
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Abstract

In this paper, a creative dung beetle optimization (CDBO) algorithm is proposed and applied to the offline parameter identification of permanent magnet synchronous motors. First, in order to uniformly initialize the population state and increase the population diversity, a strategy to improve the initialization of the dung beetle population using Singer chaotic mapping is proposed to improve the global search performance; second, in order to improve the local search performance and enhance the convergence accuracy of the algorithm, a new dung beetle position update strategy is designed to increase the spatial search range of the algorithm. Simulation results show that the proposed optimization algorithm can quickly and accurately identify parameters such as resistance, inductance, and magnetic chain of the PMSM, with significant improvements in convergence algebra, identification accuracy and stability.
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Authors and Affiliations

Xiaoliang Yang
1 2
ORCID: ORCID
Yuyue Cui
1 2
Lianhua Jia
3
Zhihong Sun
3
Peng Zhang
3
ORCID: ORCID
Jiane Zhao
4
Rui Wang
1 2
ORCID: ORCID

  1. School of Electrical and Information Engineer, Zhengzhou University of Light Industry, Zhengzhou, China
  2. Henan Key Lab of Information based Electrical Appliances, Zhengzhou, China
  3. China Railway Engineering Equipment Group Co. Ltd, Zhengzhou, China
  4. School of Electrical and Electronic Engineering, Zhengzhou University of Science and Technology, Zhengzhou, China
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Abstract

The artificial bee colony (ABC) intelligence algorithm is widely applied to solve multi-variable function optimization problems. In order to accurately identify the parameters of the surface-mounted permanent magnet synchronous motor (SPMSM), this paper proposes an improved ABC optimization method based on vector control to solve the multi-parameter identification problem of the PMSM. Because of the shortcomings of the existing parameter identification algorithms, such as high computational complexity and data saturation, the ABC algorithm is applied for the multi-parameter identification of the PMSM for the first time. In order to further improve the search speed of the ABC algorithm and avoid falling into the local optimum, Euclidean distance is introduced into the ABC algorithm to search more efficiently in the feasible region. Applying the improved algorithm to multi-parameter identification of the PMSM, this method only needs to sample the stator current and voltage signals of the motor. Combined with the fitness function, the online identification of the PMSM can be achieved. The simulation and experimental results show that the ABC algorithm can quickly identify the motor stator resistance, inductance and flux linkage. In addition, the ABC algorithm improved by Euclidean distance has faster convergence speed and smaller steady-state error for the identification results of stator resistance, inductance and flux linkage.
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Bibliography

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[19] Zawilak T., Influence of rotor’s cage resistance on demagnetization process in the line start permanent magnet synchronous motor, Archives of Electrical Engineering, vol. 69, no. 2, pp. 249–258 (2020), DOI: 10.24425/aee.2020.133023.

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Authors and Affiliations

Chunli Wu
1
ORCID: ORCID
Shuai Jiang
1
Chunyuan Bian
1

  1. College of Information Science and Engineering, Northeastern University, China

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