Adaptive Decoupling Control Using Radial Basis Function Neural Network for Permanent Magnet Synchronous Motor Considering Uncertain and Time-Varying Parameters

Jie, Hongyu; Zheng, Gang; Zou, Jianxiao; Xin, Xiaoshuai; Guo, Lei

doi:10.1109/access.2020.2993648

Cited by 26 publications

(15 citation statements)

References 26 publications

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“…where N represents the number of input variables. (2) RBF RBF is used to fit multivariate functions using discrete data [15]- [17]. Typically, RBF is used as a Gaussian function, expressed as follows:…”

Section: A Surrogate Modelmentioning

confidence: 99%

An Improved Dynamic Model Updating Method for Multistage Gearbox Based on Surrogate Model and Sensitivity Analysis

Qin

Liu

et al. 2021

IEEE Access

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Model updating can improve the accuracy of the multistage gearbox dynamic model (MGDM). However, owing to the time-consuming calculations and numerous parameters of the MGDM, previous updating methods are not suitable for updating the MGDM. To solve the above problems, an improved dynamic model updating method that is suitable for updating the MGDM is proposed in this paper. Compared with previous methods, this updating method presents two improvements: (1) In the iterative calculation process, the surrogate model is used to replace the time-consuming MGDM, thereby solving the problem of low computational efficiency; (2) before the updating calculation, Sobol sensitivity analysis is performed to screen out updating parameters from numerous parameters. This study shows that the accuracy of the updated MGDM improved by 18.52% compared with the initial model, thereby enabling a more accurate prediction of the dynamic characteristics of the actual system. This proves the effectiveness of the improved updating method.INDEX TERMS Gear, gearbox, dynamic model, updating method, sensitivity analysis, surrogate model.

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Section: A Surrogate Modelmentioning

confidence: 99%

An Improved Dynamic Model Updating Method for Multistage Gearbox Based on Surrogate Model and Sensitivity Analysis

Qin

Liu

et al. 2021

IEEE Access

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“…This kind of the control method does not have high requirements for the mathematical model. At present, it has many successful applications (Li et al, 2019a;Jie et al, 2020), such as NNIS. This method is an important branch of intelligent decoupling control of the PMSM.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Decoupling Control Study of PMSM Based on the Neural Network Inverse System

Gong

Zhi-Qiang²,

Wang³

et al. 2022

Front. Energy Res.

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This study obtains the analytical inverse system of a permanent magnet synchronous motor (PMSM) model based on the traditional magnetic field orientation decoupling control mode by analyzing the inverse quality of the PMSM. Using the neural network’s excellent approximation ability and well learning functions, a neural network inverse system (NNIS) of the decoupling control system was established by identifying and offline training the back propagation neural network (BPNN) and radial basis function neural network (RBFNN). The data collected from the analytical inverse system of the PMSM model are used to analyze and compare the prediction accuracy and running time of the neural network, so as to optimize the structure and parameters of the neural network. The simulation results of three PMSM decoupling control systems show that the PMSM decoupling control system based on RBF NNIS has good dynamic and static decoupling performance, and robustness.

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“…This kind of intelligent algorithm transforms IPMSM parameter identification problem into a system optimization problem. The algorithm mainly includes neural network algorithm [11,12], particle swarm optimization algorithm (PSO) [13,14], genetic algorithm (GA) [15,16], etc. Among them, the neural network algorithm needs a large number of samples to train the algorithm, which will increase the time cost.…”

Section: Introductionmentioning

confidence: 99%

Feedforward Decoupling Control of Interior Permanent Magnet Synchronous Motor With Genetic Algorithm Parameter Identification

Pan¹,

Liu²,

Zhu³

et al. 2021

PIER M

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Adaptive Decoupling Control Using Radial Basis Function Neural Network for Permanent Magnet Synchronous Motor Considering Uncertain and Time-Varying Parameters

Cited by 26 publications

References 26 publications

An Improved Dynamic Model Updating Method for Multistage Gearbox Based on Surrogate Model and Sensitivity Analysis

An Improved Dynamic Model Updating Method for Multistage Gearbox Based on Surrogate Model and Sensitivity Analysis

Intelligent Decoupling Control Study of PMSM Based on the Neural Network Inverse System

Feedforward Decoupling Control of Interior Permanent Magnet Synchronous Motor With Genetic Algorithm Parameter Identification

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