Continuous Control Set-Model Predictive Control for Torque Control of Induction Motors in a Wide Speed Range

Ahmed, Abdelsalam A.; Koh, Byung Kwon; Lee, Youngil

doi:10.1080/15325008.2018.1533602

Cited by 10 publications

(10 citation statements)

References 13 publications

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“…The utilized value of the power loss analysis parameters is presented in Table 5. At that condition, the instantaneous behavior of switching and conduction loss is delineated in Figure 18, using Equations (22) and 26, respectively. The average value of the switching, conduction, and harmonic losses are calculated for both the proposed and conventional controller and are shown in Figure 19.…”

Section: Comparison With Conventional Mpcmentioning

confidence: 99%

“…The MPC can be partitioned into two subsets: a continuous control set (CCS) and a finite control set (FCS) MPC [21]. In CCS MPC, a pulse width modulation (PWM)-based modulator is utilized to transmit the output from the controller [22]. The modulator provides the switching signal to the The MPC can be partitioned into two subsets: a continuous control set (CCS) and a finite control set (FCS) MPC [21].…”

Section: Introductionmentioning

confidence: 99%

“…The modulator provides the switching signal to the The MPC can be partitioned into two subsets: a continuous control set (CCS) and a finite control set (FCS) MPC [21]. In CCS MPC, a pulse width modulation (PWM)-based modulator is utilized to transmit the output from the controller [22]. The modulator provides the switching signal to the converter.…”

Section: Introductionmentioning

confidence: 99%

“…Waveforms of the instantaneous conduction and switching losses in MPC using Equations(22) and(26) respectively. Waveforms of the instantaneous conduction and switching losses in MPC using Equations(22) and(26) respectively.…”

mentioning

confidence: 99%

“…Waveforms of the instantaneous conduction and switching losses in MPC using Equations(22) and(26) respectively. (a) Conduction, (b) switching, and (c) harmonic loss in the conventional FCS-MPC and the proposed FCS-MPC based controller.…”

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confidence: 99%

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Power Loss Analysis of Solar Photovoltaic Integrated Model Predictive Control Based On-Grid Inverter

et al. 2020

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This paper presents a finite control-set model predictive control (FCS-MPC) based technique to reduce the switching loss and frequency of the on-grid PV inverter by incorporating a switching frequency term in the cost function of the model predictive control (MPC). In the proposed MPC, the control objectives (current and switching frequency) select an optimal switching state for the inverter by minimizing a predefined cost function. The two control objectives are combined with a weighting factor. A trade-off between the switching frequency (average) and total harmonic distortion (THD) of the current was utilized to determine the value of the weighting factor. The switching, conduction, and harmonic losses were determined at the selected value of the weighting factor for both the proposed and conventional FCS-MPC and compared. The system was simulated in MATLAB/Simulink, and a small-scale hardware prototype was built to realize the system and verify the proposal. Considering only 0.25% more current THD, the switching frequency and loss per phase were reduced by 20.62% and 19.78%, respectively. The instantaneous overall power loss was also reduced by 2% due to the addition of a switching frequency term in the cost function which ensures a satisfactory empirical result for an on-grid PV inverter.

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Section: Comparison With Conventional Mpcmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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Power Loss Analysis of Solar Photovoltaic Integrated Model Predictive Control Based On-Grid Inverter

et al. 2020

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Model predictive sliding mode control of six‐phase induction motor using nine‐switch converter

Gülbudak

Gökdağ

Kömürcügil

2022

Circuit Theory & Apps

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This article proposes a new control strategy to regulate the six-phase induction machine fed by a nine-switch converter. The traditional model predictive control method is a favorable feedback control routine in variable-drive applications. However, the lack of the term that assess the closed-loop stability in the conventional cost function formulation may cause potential closed-loop instability and be incapable of tolerating the parameter mismatch. Furthermore, the conventional model predictive control formulation cannot ensure global optimality in case of deviation from the nominal operating point. In this paper, the closed-loop stability is enhanced by including a sliding mode stability term in the objective function. The traditional cost function is redeveloped as a sliding mode control stability term, and the selection of the control input that satisfies the closed-loop loop stability condition is ensured. The robustness performance of the system is improved, and a better steady-state performance is achieved. The proposed control strategy is proved using a hardware setup including a six-phase 30 spatially shifted induction motor and nine-switch converter prototype. The experimental results demonstrate that a secured sixphase induction motor operation is guaranteed while improving the closedloop robustness.

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Model Predictive Control for AC Motors

Han

Gong

Gao

2022

Model Predictive Control for AC Motors

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Continuous Control Set-Model Predictive Control for Torque Control of Induction Motors in a Wide Speed Range

Cited by 10 publications

References 13 publications

Power Loss Analysis of Solar Photovoltaic Integrated Model Predictive Control Based On-Grid Inverter

Power Loss Analysis of Solar Photovoltaic Integrated Model Predictive Control Based On-Grid Inverter

Model predictive sliding mode control of six‐phase induction motor using nine‐switch converter

Model Predictive Control for AC Motors

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