Modeling and Identification of Permanent Magnet Synchronous Motor via Deterministic Learning

Yu, Wei; Liang, Henghui; Dong, Xunde; Luo, Ying

doi:10.1109/access.2020.3020848

Cited by 8 publications

(4 citation statements)

References 29 publications

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“…Using the proposed IDE algorithm above, an identification experiment of fractional‐order system is performed on a permanent magnet synchronous motor. In the experiment, the period of PRBS is 0.003 s, and the sequence length is 127 [44]. Applying this input PRBS sequence, the PMSM velocity system can be fully excited to achieve the accurate identification of the system model behavior.…”

Section: The Ide Algorithm For Fractional‐order Model Identificationmentioning

confidence: 99%

Fractional‐order system identification based on an improved differential evolution algorithm

Liang

Chen

et al. 2021

Asian Journal of Control

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With the continuous increasing of system modeling accuracy and control performance requirements, the fractional‐order models are applied to describe the dynamic characteristics of the real systems more accurately over traditional integer‐order models. Traditional identification algorithms for fractional‐order system require stringent information on the derivatives of the objective function and initial values of the parameters, which is difficult in practical applications. The differential evolution (DE) algorithm has the advantage of simple implementation and superior performance for solving multi‐dimensional complex optimization problems which is suitable for the identification of fractional‐order systems. Aiming at improving the solution accuracy and convergence speed, an improved DE (IDE) algorithm for fractional‐order system identification is proposed. The movement strategy of the marine predator algorithm (MPA) and the mutation strategy of randomly selecting one from the optimal individual population as the basis vector are both adopted in this proposed IDE algorithm. The parameter information of the successfully mutated individual is archived during the iteration process. The scaling factor and crossover probability factor are adaptively adjusted according to the archived information to enhance the best solution accuracy obtained in the search process. By testing 13 sets of unimodal and multimodal functions with high‐performance optimization algorithms, the accuracy performance of the proposed IDE solution is verified. The IDE algorithm is applied to identify the parameters of the fractional‐order system of permanent magnet synchronous motor with simulation and experiment. The identification results clearly show the effectiveness of the proposed methodology.

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Section: The Ide Algorithm For Fractional‐order Model Identificationmentioning

confidence: 99%

Fractional‐order system identification based on an improved differential evolution algorithm

Liang

Chen

et al. 2021

Asian Journal of Control

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“…In order to obtain reliable permanent magnet synchronous motor parameters, suitable parameter identification methods are required, and the main methods for permanent magnet synchronous motor parameter identification are: model reference adaptive algorithm [8,9], recursive least squares method [10], extended Kalman filter algorithm [11], artificial neural network algorithm [12], particle swarm optimization [13,14]. With the development of machine learning [15,16], deterministic learning is applied to parameter identification of permanent magnet synchronous motor [17].…”

Section: Introductionmentioning

confidence: 99%

Double Update Intelligent Strategy for Permanent Magnet Synchronous Motor Parameter Identification

Zhou¹,

Wang²,

Du³

et al. 2023

Computers, Materials &Amp; Continua

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The parameters of permanent magnet synchronous motor (PMSM) affect the performance of vector control servo system. Because of the complexity of nonlinear model of PMSM, it is very difficult to identify the parameters of PMSM. Aiming at the problems of large amount of data calculation, low identification accuracy and poor robustness in the process of multi parameter identification of permanent magnet synchronous motor, this paper proposes a weighted differential evolutionary particle swarm optimization algorithm based on double update strategy. By introducing adaptive judgment factor to control the proportion of weighted difference evolution (WDE) algorithm and particle swarm optimization (PSO) algorithm in each iteration process, and consider using PSO algorithm or WDE algorithm to update individuals according to the probability law. The individuals obtained from WDE operation are used to guide the individual evolution process in PSO operation through the information exchange mechanism. The proposed WDEPSO algorithm can ensure the diversity and effectiveness of the individual evolution of the population. The algorithm is applied to parameter identification of PMSM drive system. The simulation results show that the proposed algorithm has better convergence performance and has strong robustness, parameter identification of permanent magnet synchronous motor based on proposed method does not need to rely on more data sheet on the motor design value, can motor stator resistance identification at the same time, the rotor flux linkage, d/q-axis inductance and electrical parameters, and can effectively track the parameters value.

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“…The least square-based method has received a lot of attention owing to its simple principle and good performance [5]. The extended Kalman filter and neural network are suitable for solving the problems that are nonlinear in nature, and thus they are used in many online parameter adaption algorithms [8][9][10][11]. Ref.…”

Section: Introductionmentioning

confidence: 99%

An improved deadbeat predictive current control for induction motor drives

Wang

Zhang

Yang

2022

IET Power Electronics

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Deadbeat predictive current control (DPCC) has been widely studied in induction motor drives due to its simple concept, quick response and excellent steady‐state performance. However, conventional DPCC uses a lot of motor parameters to predict current and voltage. Therefore, the performance of DPCC is affected by the accuracy of motor parameters in the controller. The performance of DPCC will severely deteriorate when the motor parameters used in the controller deviate greatly from their actual values. To improve the parameter robustness of DPCC, this paper proposes a robust DPCC (RDPCC), which utilizes the error between the measured currents and predicted currents to compensate for the influence of machine parameter mismatches. The proposed method is compared with the conventional DPCC, and the experimental results confirm its strong robustness against motor parameter mismatch.

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Modeling and Identification of Permanent Magnet Synchronous Motor via Deterministic Learning

Cited by 8 publications

References 29 publications

Fractional‐order system identification based on an improved differential evolution algorithm

Fractional‐order system identification based on an improved differential evolution algorithm

Double Update Intelligent Strategy for Permanent Magnet Synchronous Motor Parameter Identification

An improved deadbeat predictive current control for induction motor drives

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