Continuous Control Set Predictive Current Control for Induction Machine

Varga, Toni; Benšić, Tin; Štil, Vedrana Jerković; Barukčić, Marinko

doi:10.3390/app11136230

Cited by 7 publications

(6 citation statements)

References 33 publications

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“…While linear feedback control, such as any variant from the classical PID-style field-oriented approaches, is known for its limited transient control performance, model predictive control (MPC) renders itself the most promising alternative. However, existing predictive torque control approaches very often do not consider the problem of reduced rotor flux at partial load with respect to the transient response, i.e., they always operate at the nominal flux level, leading to suboptimal drive efficiency in steady state [5]- [12]. Others use strongly simplified heuristic rules for the i sd -i sq trade-off during torque transients, e.g., always prioritizing the flux-forming current component [13]- [15], leading to strongly suboptimal transient torque performance.…”

Section: A State Of the Artmentioning

confidence: 99%

“…Only few prior publications investigated the transient torque control problem when operating at partial rotor flux: While [16] proposes a model predictive torque control (MPTC) that includes variable rotor flux excitation, it only considers the simplified linear IM model without any non-linear impacts such as magnetic saturation, iron losses, skin effect, or other parameter-varying effects such as thermal influences. In addition, [12] studies a similar problem based on a hierarchical control approach to obtain extended prediction horizons, but also only considers a linear IM model and does not meet the optimal steady-state operating points exactly.…”

Section: A State Of the Artmentioning

confidence: 99%

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Nonlinear Efficiency-Optimal Model Predictive Torque Control of Induction Machines

Becker,

Stender,

Wallscheid

2023

Preprint

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<p>Induction machines (IMs) are widely used in many applications, e.g., electric vehicles or industrial automation, which motivates efficiency-optimal operation for the sake of energy and cost savings. However, a loss-minimal IM control leads to reduced flux operation at partial load, i.e., achievable torque dynamics after load steps are physically limited due to the IM’s large rotor time constant. Hence, designing the IM torque control for minimal settling time is of particular importance to allow both an efficient and sufficiently dynamic drive operation. Against this background, a model predictive control (MPC) framework is proposed, which utilizes a precise model of the IM covering magnetic saturation, iron losses, skin effect and thermal influences. As this model is highly nonlinear, it is iteratively linearized along the predicted control trajectory so that a computationally efficient quadratic program can be defined for the MPC problem. To enable sufficiently long prediction horizons, a hierarchical control structure with the stator currents as the actuation variables of the model predictive torque control is utilized. Thanks to this scalable control approach, the proposed framework can be easily extended to multi-machine drive systems covering higher-dimensional problem spaces. Empirical tests validate the feasibility of the proposed approach both for single as well as multi-machine drive applications.</p>

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Section: A State Of the Artmentioning

confidence: 99%

Section: A State Of the Artmentioning

confidence: 99%

Nonlinear Efficiency-Optimal Model Predictive Torque Control of Induction Machines

Becker,

Stender,

Wallscheid

2023

Preprint

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“…• ω e ω e−rated (9) where R m-rated is the R m value at rated frequency and magnetization (for the considered IM, R m-rated = 1258.3 Ω for accounted SLLs, and R m-rated = 1012.3 Ω for neglected SLLs), ω e-rated denotes the rated stator angular frequency, and K h (ψ s ) is the stator flux-dependent hysteresis loss coefficient (given in Appendix B), whose value differs depending on whether the SLLs are accounted or not [27].…”

Section: Iron Loss Modelingmentioning

confidence: 99%

“…Particularly, the direct MPC (DMPC) (also known as the finite control set MPC) has been favored in the power electronics community due to its design simplicity and the possibility to include system constraints [5]. The indirect MPC (also known as the continuous MPC), on the other hand, offers constant switching frequencies and is gaining attention, especially for grid-tie converters, but it requires a PWM modulator [9,10]. In DMPC, the future behavior of the system is predicted based on the corresponding model and a finite number of switch positions, whereas the optimal position is found through minimization of the cost function and directly applied to the converter, thus eliminating an intermediate PWM stage.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Current Control of an Induction Motor Considering Iron Core Losses and Saturation

Bašić,

Vukadinović,

Grgić

2023

Processes

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The paper considers the model predictive current control (MPCC) of an induction motor (IM) drive and evaluates five IM models of different complexities—from conventional to magnetic saturation, iron losses, and stray-load losses—for the MPCC design. The validity of each considered IM model and the corresponding MPCC algorithm is evaluated by comparison of the following performance metrics: the total harmonic distortion of the stator current, the average switching frequency, the rotor flux magnitude error, the rotor flux angle error, and the product of the first two metrics. The metrics’ values are determined in wide ranges of the rotor speed (0.1–1 p.u.) and load torque (0–1 p.u.) through simulations performed in the MATLAB Simulink environment. The obtained results allow us to identify the IM model that offers the best tradeoff between the practicability and accuracy. Furthermore, a control effort penalization (CEP) is suggested to reduce the average switching frequency and, hence, the power converter losses. This involves constraining the simultaneous switching to a maximum of two branches of the three-phase power converter, as well as inclusion of the weighted switching penalization term in the cost function. Finally, the performance—both steady-state and dynamic—of the proposed MPCC system with CEP is compared with that of the analogous field-oriented controlled (FOC) IM drive. The inverter switching frequency is reduced more than twice by including the frequency-dependent iron-loss resistance in the MPCC. It is additionally reduced by implementing the proposed CEP strategy without sacrificing many other performance metrics, thus achieving a performance comparable to the FOC IM drive.

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“…The induction motor (IM) has been widely used in electric drive systems due to the advantages of easy maintenance and high reliability [1][2][3][4]. Vector control (VC) and direct torque control (DTC) are currently the most widely used high-performance control methods in the industry [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

A Low Switching Frequency Model Predictive Control Method for an Induction Motor Fed by a 3-Level Inverter

Huang,

Jiang,

Zhang

et al. 2023

Electronics

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Traditional model predictive control (MPC) for the induction motor fed by a three-level inverter needs to explore 27 voltage vectors to obtain the optimal one, which leads to high switching frequency and requires too much computation. To solve this issue, a low switching frequency model predictive control with partition optimization is proposed. First, the reference voltage vector can be gained from the prediction model at the next time, and the space voltage vector plane is divided into 12 sectors for further vector choice. Furthermore, considering inverter constraints, the candidate voltage vectors are determined according to the sector location of the reference voltage vector. In this way, the candidate vectors can be reduced to 3 at most. Then, a boundary circle limit is designed to avoid unnecessary switch changes. If the reference voltage vector is within the boundary limit, the switches do not act, which can reduce the system switching frequency without introducing the extra weight coefficient into the cost function. These selected voltage vectors are substituted into the cost function to determine the optimal one. Finally, the neutral point voltage deviation is controlled by the positive and negative redundant small vectors to realize the multi-objective constraint without weighting coefficients. The simulation results show that the proposed control method can significantly reduce the switching frequency; at the same time, both the dynamic and steady performances can be maintained well, and the cost function has no weight coefficients.

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Continuous Control Set Predictive Current Control for Induction Machine

Cited by 7 publications

References 33 publications

Nonlinear Efficiency-Optimal Model Predictive Torque Control of Induction Machines

Nonlinear Efficiency-Optimal Model Predictive Torque Control of Induction Machines

Model Predictive Current Control of an Induction Motor Considering Iron Core Losses and Saturation

A Low Switching Frequency Model Predictive Control Method for an Induction Motor Fed by a 3-Level Inverter

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