A Comparison on Finite-Set Model Predictive Torque Control Schemes for PMSMs

Sandre-Hernández, Omar; Rangel-Magdaleno, José; Morales-Caporal, Roberto

doi:10.1109/tpel.2017.2777973

Cited by 72 publications

(38 citation statements)

References 32 publications

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“…1) Choice of Norm: When short-horizon direct MPC is employed, then a popular design choice is to use the ℓ 1norm, see, e.g., [13], [21], [123], [126], [139], [140]. When the available computational power is of concern, such a choice is reasonable, since computation of the absolute values of the variable of concern is computationally cheap and straightforward.…”

Section: A Objective Functionmentioning

confidence: 99%

Model Predictive Control of Power Electronic Systems: Methods, Results, and Challenges

Καραμανάκος

Liegmann

Geyer

et al. 2020

IEEE Open J. Ind. Applicat.

302

130

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Model predictive control (MPC) has established itself as a promising control methodology in power electronics. This survey paper highlights the most relevant MPC techniques for power electronic systems. These can be classified into two major groups, namely, MPC without modulator, referred to as direct MPC, and MPC with a subsequent modulation stage, known as indirect MPC. Design choices and parameters that affect the system performance, closed-loop stability and controller robustness are discussed. Moreover, solvers and control platforms that can be employed for the real-time implementation of MPC algorithms are presented. Finally, the MPC schemes in question are assessed, among others, in terms of design and computational complexity, along with their performance and applicability depending on the power electronic system at hand.

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Section: A Objective Functionmentioning

confidence: 99%

Model Predictive Control of Power Electronic Systems: Methods, Results, and Challenges

Καραμανάκος

Liegmann

Geyer

et al. 2020

IEEE Open J. Ind. Applicat.

302

130

View full text Add to dashboard Cite

show abstract

“…This simplification is commonly applied to digital models, e.g. in model predictive control (Sandre-Hernandez et al, 2018;Wang et al, 2018). However, in low SFFR drives, the duration of the PWM cycle may constitute a substantial part of the phase current period, and so the current can notably change in the PWM cycle.…”

Section: Using the Current Value I Amentioning

confidence: 99%

Derivation of Motor Mean Phase Currents in PMSM Drives Operating with Low Switching-to-Fundamental Frequency Ratio

Jarzębowicz

2019

Power Electronics and Drives

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Pulse width modulation (PWM) of inverter output voltage causes the waveforms of motor phase currents to consist of distinctive ripples. In order to provide suitable feedback for the motor current controllers, the mean value must be extracted from the currents’ waveforms in every PWM cycle. A common solution to derive the mean phase currents is to sample their value at the midpoint of a symmetrical PWM cycle. Using an assumption of linear current changes in steady PWM subintervals, this midpoint sample corresponds to the mean current in the PWM cycle. This way no hardware filtering or high-rate current sampling is required. Nevertheless, the assumption of linear current changes has been recently reported as over simplistic in permanent magnet synchronous motor (PMSM) drives operating with low switching-to-fundamental frequency ratio (SFFR). This, in turn, causes substantial errors in the representation of the mean phase currents by the midpoint sample. This paper proposes a solution for deriving mean phase currents in low SFFR PMSM drives, which does not rely on the linear current change assumption. The method is based on sampling the currents at the start point of a PWM cycle and correcting the sampled value using a model-based formula that reproduces the current waveforms. Effectiveness of the method is verified by simulation for an exemplary setup of high-speed PMSM drive. The results show that the proposed method decreases the error of determining the mean phase currents approximately 10 times when compared to the classical midpoint sampling technique.

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“…The reluctance torque was utilized completely while the operating efficiency was improved immensely. In recent years, a kind of model predictive torque control (MPTC) method is attracting a lot of attention in the academic and industry communities because it can observably improve the torque dynamic performance [29]- [31]. Zhang et al presented a model predictive torque control method with optimal duty cycle control for induction motor [32], and the better steadstate performance under the whole speed region is achieved.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Torque Control of a Hybrid Excited Axial Field Flux-Switching Permanent Magnet Machine

2020

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Hybrid excited axial field flux-switching permanent magnet (HEAFFSPM) machine is a novel stator excitation hybrid excited synchronous machine, which combines the advantages of the axial field flux-switching permanent magnet machine and wound field machine. In this paper, two model predictive torque control (MPTC) methods with flux-adjusting strategy implementation for the HEAFFSPM machine, including the MPTC and MPTC with duty cycle control (MPTC-DCC) by optimizing the active voltage vector duration to reduce the torque and flux ripples, are proposed and comparatively investigated. Based on the theoretical analysis, a discrete-time model of the HEAFFSPM machine is established. Considering the flux-enhancing/-weakening strategies, the different multi-objective cost functions are designed. The multiple right weight coefficients are analyzed and chosen to optimize the operating performance of the drive system. The results indicate that the proposed MPTC-DCC method reduces the torque and flux ripples significantly compared with MPTC method, and the drive system has better steady performance. Meanwhile, the load capability in the whole speed region is improved and the constant power operating range is broadened by using the proposed flux-adjusting strategy. INDEX TERMS Axial field, flux-switching, flux-adjusting, model predictive torque control, duty cycle control.

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A Comparison on Finite-Set Model Predictive Torque Control Schemes for PMSMs

Cited by 72 publications

References 32 publications

Model Predictive Control of Power Electronic Systems: Methods, Results, and Challenges

Model Predictive Control of Power Electronic Systems: Methods, Results, and Challenges

Derivation of Motor Mean Phase Currents in PMSM Drives Operating with Low Switching-to-Fundamental Frequency Ratio

Model Predictive Torque Control of a Hybrid Excited Axial Field Flux-Switching Permanent Magnet Machine

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