A Novel Modulated Model Predictive Control Applied to Six-Phase Induction Motor Drives

Ayala, Magno; Doval-Gandoy, Jesús; Rodas, Jorge; González, Osvaldo; Gregor, Raúl; Rivera, Marco

doi:10.1109/tie.2020.2984425

Cited by 48 publications

(47 citation statements)

References 31 publications

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“…This fact is produced by the comparable time between the computation of the control signal and the sampling period [22]. The tuning parameter (λ xy ) expressed in (18) is usually considered in the control of the multiphase machines to provide greater weight to the α-β currents over the x-y currents.…”

Section: Cost Functionmentioning

confidence: 99%

“…Employing a cost function, it selects up to 4 vectors, two LV and two medium vectors (MV) to reduce the losses in the x-y plane. It is essential to mention that in M2PC, the cost function implements a weighting factor between current tracking and x-y loss reduction, and to some extent, it is expected that the algorithm tends to use the zero vector to reduce losses, even if the current tracking decreases in the α-β plane, so the NM2PC presents an advantage by not using the ZV, but on the other hand, the tracking ability of the controller for small currents deteriorates [18]. Further, in [19], it was shown a direct relationship between the stability of modulated MPC and the values of sampling frequency and rotor speed for multiphase machines.…”

Section: Introductionmentioning

confidence: 99%

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Algorithm for Implementation of Optimal Vector Combinations in Model Predictive Current Control of Six-Phase Induction Machines

et al. 2021

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The development of new control techniques for multiphase induction machines (IMs) has become a point of great interest to exploit the advantages of these machines compared to three-phase topology, for example, the reduced phase currents and lower harmonic contents. One of the most analyzed techniques is the model-based predictive current control (MPC) with a finite control set. This technique presents high x–y currents because of the application of one switching state throughout the whole sampling period. Nevertheless, it is one of the most used due to its excellent dynamic response. To overcome the aforementioned drawbacks, new techniques called virtual vectors have been developed, but although there are several articles with experimental results, the algorithm for implementing the technique has not been appropriately described. This document provides a clear and detailed explanation for algorithm implementation of virtual vectors through two proposed variants VV4 and VV11, in a six-phase machine drive. The first entails lower computational cost and the second lower loss in the x–y plane. According to performance indicators such as the total harmonic distortion and the mean square error for both case studies, experimental tests were evaluated to determine the implementation’s behaviour.

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Section: Cost Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Algorithm for Implementation of Optimal Vector Combinations in Model Predictive Current Control of Six-Phase Induction Machines

et al. 2021

Self Cite

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“…2 = 2( 4 + 5 + 6 ) + 7 (23) From Equations ( 23) and (24), it is obvious that the rectifier switching states change all the time, when is zero, simplifying the IMC commutation strategy.…”

Section: Optimal Switching Sequencementioning

confidence: 99%

“…However, FCS-MPC does not involve a modulation scheme, in which the optimal switching states selected by the cost function may continue to be optimal for the following several sampling instances; thus, the switching frequency is variable, resulting in broad harmonics. To improve this, research considering the combination of FCS-MPC and modulation has been conducted [16][17][18][19][20][21][22][23][24]. In [16], an indirect model predictive control strategy was proposed for DMCs, in which the imposed sinusoidal current waveforms and the reactive power were considered individually; only simulations were implemented.…”

Section: Introductionmentioning

confidence: 99%

“…In [22], a time-modulated, model-predictive control strategy was proposed for a neutral point clamped (NPC) converter, which can be operated at a 20 kHz sampling frequency. In [23], a novel M 2 PC strategy was proposed for a six-phase induction motor, where SVM was used to reduce the steady-state error and improve the (x-y) currents at high operating speeds. In [24], FCS-MPC was proposed for ac-dc matrix converters, where the virtual space vectors were preselected to reduce the calculation efforts, and the effect of parameter mismatch was analyzed.…”

Section: Introductionmentioning

confidence: 99%

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Continuous Control Set Model Predictive Control for an Indirect Matrix Converter

Zhang

2021

Energies

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A continuous control set model predictive power control strategy for an indirect matrix converter is proposed in this paper. The load reactive power, the load active power, and the input reactive power are controlled simultaneously. This control strategy can obtain output waveforms with fixed switching frequency. Additionally, an optimal switching sequence is proposed to simplify the commutations of the indirect matrix converter. To suppress the input filter resonance, an active damping method is proposed. Experimental results prove that the proposed method features controllable input reactive power, controllable load active and reactive power, fixed switching frequency output waveforms, zero-current switching operations, and effectively suppresses input filter resonance.

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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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A Novel Modulated Model Predictive Control Applied to Six-Phase Induction Motor Drives

Cited by 48 publications

References 31 publications

Algorithm for Implementation of Optimal Vector Combinations in Model Predictive Current Control of Six-Phase Induction Machines

Algorithm for Implementation of Optimal Vector Combinations in Model Predictive Current Control of Six-Phase Induction Machines

Continuous Control Set Model Predictive Control for an Indirect Matrix Converter

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

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