Adaptive two-degree-of-freedom PI for speed control of permanent magnet synchronous motor based on fractional order GPC

Qiao, Wenjun; Tang, Xiaoqi; Zheng, Shiqi; Xie, Yuanlong; Song, Bao

doi:10.1016/j.isatra.2016.06.008

Cited by 27 publications

(13 citation statements)

References 26 publications

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“…Анализ методов управления серводвигателями позволяет сделать вывод о том, что в основе таких систем лежит скалярное и векторное управление данным типом электродвигателей. Расчет отклонения фактической скорости от заданной и соответствующая коррекция ошибки может осуществляться с использованием современных методов решения данной задачи с использованием нейронных сетей, нечеткой логики и стандартной настройки с использованием двух ПИ-регуляторов [5][6][7][8][9][10][11][12][13]. Наиболее перспективным [12,14] является векторное управление с использованием датчика положения ротора, схема которого показана на рис.…”

Section: рис 5 структура электропривода с векторным управлением и дunclassified

To the question of control of servomotors of automatic welding unit designed for welding of large-sized tanks of launch vehicles

Ibatullin¹,

Gebel²,

Gudinov³

2019

OSB

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а. а. ибаТУллиН е. с. гебель В. Н. гУДиНоВ Омский государственный технический университет, г. Омск К ВоПросУ УПраВлеНия серВоДВигаТелями аВТомаТичесКой сВАРОЧНОй устАНОВКИ, ПреДНаЗНачеННой Для сВарКи КрУПНогабариТНых ёмКосТей раКеТ-НосиТелей В статье рассматривается процесс управления сервоприводами с целью регулирования скорости и обеспечения синхронного вращения валов силовых агрегатов при сварке с использованием сварочной установки сБ-2000. На основе технологического процесса сварки крупногабаритных емкостей ракет-носителей модернизированы основные силовые агрегаты. Предлагается структурное решение для управления технологическим процессом и синхронизации работы электроприводов с использованием серводвигателей. Разработаны блок-схема алгоритма и техническое решение управления сервоприводами с использованием модулей SINAMICS S120 фирмы SIEMENS. Ключевые слова: автоматизированная система управления технологическим процессом, распределенная система управления, программируемый логический контроллер, электропривод с векторным управлением, синхронный серводвигатель, преобразования Парка и Кларк.

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Section: рис 5 структура электропривода с векторным управлением и дunclassified

To the question of control of servomotors of automatic welding unit designed for welding of large-sized tanks of launch vehicles

Ibatullin¹,

Gebel²,

Gudinov³

2019

OSB

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“…Different strategies and approaches to the synthesis of the current (torque) control loop for the PMSM have been developed and researched. A classic and quite effective solution is the use of a linear or modified proportional-integral controller [8,[10][11][12]. The most known techniques for tuning the current (torque) control loop require knowledge of the electrical parameters of the PMSM, particularly the phase resistances and inductances.…”

Section: Introductionmentioning

confidence: 99%

Modified Technique of Parameter Identification of a Permanent Magnet Synchronous Motor with PWM Inverter in the Presence of Dead-Time Effect and Measurement Noise

et al. 2019

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The paper considers the problem of parameter identification of the surface mounted permanent magnet synchronous motor (SPMSM) with pulse width modulated (PWM) inverter in the presence of dead time of power switches and other nonlinear distortions. Parameter identification of the SPMSM is required for the tuning of the torque control loop, because in some cases, the exact values of phase resistances and inductances are not known. In the absence of nonlinear disturbances, the problem of SPMSM parameters estimation is not difficult. The influence of the dead-time effect, back electromotive force and measurements noise introduces distortions in experimental output data sets, which leads to incorrect parameter estimation. Thus, there is a need to develop new designs of identification experiments and methods of processing of the experimental data. A detailed mathematical model of SPMSM with a PWM inverter in the presence of dead-time effect is considered in the paper. The negative influence of the dead-time effect on the results of parameter estimation is shown. A modified technique of parameter identification of SPMSM based on the estimation of frequency response function is proposed. The applied design of identification experiments, the type of excitation input signal, and methods of data processing allow us to minimize the influence of nonlinear disturbances and to reduce the variance of estimation of frequency response function. These features provide a high performance of SPMSM parameters estimation.

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“…Cascade control with PI still has problems related to impossible performance optimization and difficult fine tuning for improving the dynamic performance or the transient response of the motor. Because PMSM has nonlinear behavior, unmeasured disturbance like load torque, and parameter variations like friction force and rotor inertia, advanced nonlinear control, and disturbance rejection methods were proposed to compensate these disturbances and variations …”

Section: Introductionmentioning

confidence: 99%

“…Because PMSM has nonlinear behavior, unmeasured disturbance like load torque, and parameter variations like friction force and rotor inertia, advanced nonlinear control, and disturbance rejection methods were proposed to compensate these disturbances and variations. [1][2][3] Conventional predictive control (CPC) is basically evaluating the control signals based on minimizing the cost function which is a quadratic performance index of the error between the actual output and the predicted reference output to be tracked. This cost function cannot compensate this error completely for highly nonlinear processes, and in this case, CPC List of abbreviation and symbols: R, the stator resistance; L d , the d-axis stator inductance; L q , the q-axis stator inductance; J, inertia moment; B, viscous friction coefficient; i d , the d-axis stator current; i q , the q-axis stator current; v d , the d-axis stator voltage; v q , the q-axis stator voltage; ω s , the electrical rotor speed; λ af , the rotor permanent magnet flux; T e , the developed torque; T L , the applied load torque; k, the current sampling instant; k + 1, the sampling instant…”

mentioning

confidence: 99%

Nonlinear adaptive extended state space predictive control of permanent magnet synchronous motor

Hagras

2018

Int Trans Electr Energ Syst

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Summary This paper proposes new adaptive predictive control approach to control the current loop of the surface permanent magnet synchronous motor (SPMSM) called as extended state space predictive control. The state space model of SPMSM was extended by taking the past inputs as system state variables in the state space model. Then, a new state space model based on the state changes and output tracking error as the new states for new extended state space model was proposed for SPMSM. Accordingly, a new cost function was proposed which provided the change of the control input. Therefore, the simulation results proved that the proposed method had improved reference tracking, fast transient response, compensation of parameter variations, and disturbance attenuation compared to PI controller in field‐oriented vector control scheme.

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Adaptive two-degree-of-freedom PI for speed control of permanent magnet synchronous motor based on fractional order GPC

Cited by 27 publications

References 26 publications

To the question of control of servomotors of automatic welding unit designed for welding of large-sized tanks of launch vehicles

To the question of control of servomotors of automatic welding unit designed for welding of large-sized tanks of launch vehicles

Modified Technique of Parameter Identification of a Permanent Magnet Synchronous Motor with PWM Inverter in the Presence of Dead-Time Effect and Measurement Noise

Nonlinear adaptive extended state space predictive control of permanent magnet synchronous motor

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