Modulated Model-Free Predictive Control With Minimum Switching Losses for PMSM Drive System

Wang, Yuchen; Li, Hongmei; Yang, Liguo; Xl, Wang

doi:10.1109/access.2020.2968379

Cited by 34 publications

(22 citation statements)

References 36 publications

(34 reference statements)

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“…, m, m ≥ 2. It has been observed in [39] and confirmed by all encountered concrete case-studies (see, e.g., [75]), that such a system may usually be regulated via m monovariable ultra-local models:…”

Section: Mimo Systemsmentioning

confidence: 65%

Machine Learning and Control Engineering: The Model-Free Case

Fliesś

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2020

Proceedings of the Future Technologies Conference (FTC) 2020, Volume 1

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This paper states that Model-Free Control (MFC), which must not be confused with Model-Free Reinforcement Learning, is a new tool for Machine Learning (ML). MFC is easy to implement and should be substituted in control engineering to ML via Artificial Neural Networks and/or Reinforcement Learning. A laboratory experiment, which was already investigated via today's ML techniques, is reported in order to confirm this viewpoint.

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Section: Mimo Systemsmentioning

confidence: 65%

Machine Learning and Control Engineering: The Model-Free Case

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2020

Proceedings of the Future Technologies Conference (FTC) 2020, Volume 1

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“…First, literature [16][17][18][19][20][21][22][23][24] all show that current predictive control has higher system bandwidth than PI. Therefore, this paper designs current predictive control strategy instead of PI control.…”

Section: Design Of Spmsg's Model Predictive Current Controlmentioning

confidence: 99%

Composite control for disturbed direct-driven surface-mounted permanent magnet synchronous generator with model prediction strategy

Shi

Nie

2021

Measurement and Control

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In order to obtain the best power in the wind energy conversion system (WECS) of the direct-driven surface-mounted permanent magnet synchronous generator (SPMSG), active disturbance rejection control (ADRC) is introduced to track the motor speed in real time. The control algorithm provides a new design concept and an inherent robust controller component that requires very little system information. Aiming at the problem of system parameter mutation caused by internal factors and external environment changes, an adaptive controller with multi parameter identification is designed, and the disturbance caused by parameter changes is compensated in real time. The model predictive current control (MPC) technology for the sudden change of external environment is designed to accelerate the response speed of the current loop, so as to weaken the estimation of the current disturbance by the active disturbance rejection controller, and make the speed estimation more accurate. Simulation results show that the proposed control strategy is effective and satisfactory.

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“…A model predictive control (MPC) approach has been accepted by many scientists as a powerful control technique for power electronic converters because of its excellent performance in terms of nonlinear control, multiobjective optimization, and dynamic response. The existing literature on MPC is extensive and focuses particularly on the control of power electronic converters in various systems, including motor control applications [20], photovoltaic (PV) applications [21], grid-connected inverter applications [22], [23], etc. Furthermore, the MPC based control technique is proposed to regulate the line current as well as the dc-link voltage of the single-phase NPC rectifier [24].…”

Section: Introductionmentioning

confidence: 99%

Grid Voltage Sensorless Model Predictive Control for a Single-Phase T-Type Rectifier With an Active Power Decoupling Circuit

Bayhan

2021

IEEE Access

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This paper proposes grid voltage sensorless model predictive control for a single-phase T-type rectifier with an active power decoupling circuit. The proposed sensorless technique is based on a model reference adaptive system (MRAS) and tested under distorted grid conditions. This study also examines the relationship among the ripple energy, the dc-link capacitor, and the active power decoupling circuit capacitor. The developed control technique is proposed to ensure the following objectives; (1) sensorless grid voltage estimation; (2) the second-order ripple power elimination; (3) reference current generation based on power equilibrium; (4) ensuring unity power factor under all operating conditions; and (5) capacitor voltage balance. The developed control structure offers simplicity and it is cost-effective due to the absence of a grid voltage sensor. An experimental prototype is established, and the main results, including the steady-state and dynamic performances, are presented to validate the effectiveness of the proposed control. INDEX TERMS Sensorless control, model predictive control, active power decoupling, single-phase PWM rectifiers.

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Modulated Model-Free Predictive Control With Minimum Switching Losses for PMSM Drive System

Cited by 34 publications

References 36 publications

Machine Learning and Control Engineering: The Model-Free Case

Machine Learning and Control Engineering: The Model-Free Case

Composite control for disturbed direct-driven surface-mounted permanent magnet synchronous generator with model prediction strategy

Grid Voltage Sensorless Model Predictive Control for a Single-Phase T-Type Rectifier With an Active Power Decoupling Circuit

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