Model Predictive Control of a Switched Reluctance Machine for Guaranteed Overload Torque

Qi, Fang; Stippich, Alexander; Ralev, Iliya; Klein-Heßling, Annegret; Doncker, Rik W. De

doi:10.1109/tia.2018.2874618

Cited by 18 publications

(12 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The model predicts the instantaneous operating temperature within the next sampling periods and determines the optimal torque/current reference limitation. It has been considered for induction [76], PM, DC motors [101], and even SRMs [74].…”

Section: F Overload Controlmentioning

confidence: 99%

“…The application in SRMs gains more relevance because they can withstand thermal stress better than AC machines. It led to predictive control to handle the highly nonlinear characteristics and predict the winding temperature online, thus limiting the maximum torque to maintain a defined level of copper losses [74]. The iron losses have not been considered given the little impact they have on the lowspeed copper losses.…”

Section: F Overload Controlmentioning

confidence: 99%

See 1 more Smart Citation

Vision, Challenges, and Future Trends of Model Predictive Control in Switched Reluctance Motor Drives

et al. 2021

View full text Add to dashboard Cite

Switched Reluctance Motors (SRMs) have become a popular alternative to replace permanent magnet machines in high-performance emerging applications such as automotive and aerospace. However, its market attractiveness is limited by the difficulty in control given its nonlinear behaviour. Model predictive control (MPC) is a promising solution to deal with this problem as per its notable features to deal with complex systems, nonlinearities and constraints. Still, the applications in SRMs are at an early stage compared to other drives. This paper aims to discuss the recent advancements and challenges in MPC for SRMs and a vision of its future developments and applications. The article describes the main difficulties in SRM control and the different approaches adopted to date by MPC to solve them. It also analyzes the control objectives that should still be considered in SRM drives, their particular challenges and how recent MPC developments in other AC drives can be adapted to the SRM case. The paper then proposes a roadmap of future works to achieve a unified and reliable control strategy that boosts SRM to outperform other drives, relating the control objectives to its potential applications.

show abstract

Section: F Overload Controlmentioning

confidence: 99%

Section: F Overload Controlmentioning

confidence: 99%

Vision, Challenges, and Future Trends of Model Predictive Control in Switched Reluctance Motor Drives

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Therefore, some modifications and improvements for the MPC have been proposed and then developed by the researchers in order to solve the aforementioned problems [19]. Regarding to the implementation of the MPC, different types of control platforms have been used like digital signal processors (DSPs) [20]- [22], dSPACE semi-physical simulation system [12], [23], [24], field programmable gate arrays (FPGAs) [25]- [28], etc. These control platforms are based on the use of microprocessor units with exponentially increasing calculation ability.…”

Section: Introductionmentioning

confidence: 99%

Recent Achievements in Model Predictive Control Techniques for Industrial Motor: A Comprehensive State-of-the-Art

Elmorshedy

El-Sousy

et al. 2021

IEEE Access

View full text Add to dashboard Cite

His research interests include modeling and control of motor drives, motion-control systems, wind energy systems, digital signal processing-based computer control systems, computational intelligent of power electronics, electric drives and power systems, intelligent control theories including fuzzy logic, neural networks, and wavelets, nonlinear control, optimal control, and robust control. MD. RABIUL ISLAM (Senior Member, IEEE) received the Ph.D. degree in electrical engineering from the

show abstract

“…Recently, model predictive control (MPC) has been a novel scheme for nonlinear system, which uses a predict model to calculate the value of next sampling time of the motor [16–22]. At present, many scholars have proposed multiple MPC strategies for SRM drives.…”

Section: Introductionmentioning

confidence: 99%

Minimization of torque ripple in switched reluctance motor based on MPC and TSF

Ren

Zhu

Jing

et al. 2021

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

In this paper, a novel control method based on model predictive control (MPC) and torque sharing function (TSF) is addressed to minimize the torque ripple of switched reluctance motor (SRM). Firstly, an accurate SRM model is established based on flux‐linkage characteristic curves obtained from the locked rotor test, which can predict the future operation state of the SRM drive system. Secondly, an improved TSF curve is proposed, and genetic algorithm is used to optimize TSF parameters to reduce torque ripple in commutation region. Besides, the MPC is integrated into the TSF control framework to replace the traditional hysteresis controller, and establishes a new TSF‐based model predicted torque control (MPTC) method, which avoids the frequency conversion problem caused by torque or current hysteresis controller. Further, a sector division scheme is addressed to decrease the number of candidate voltage states, whereby the computation of the controller in each sampling period is effectively reduced. Finally, in order to verify the correctness of the MPTC method, it is compared with the traditional direct instantaneous torque control (DITC). Both simulations and experiments on a three‐phase 12/8 pole SRM have confirmed that MPTC scheme can not only decreases torque ripple and stator copper losses, but also has higher efficiency than DITC. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

show abstract

Model Predictive Control of a Switched Reluctance Machine for Guaranteed Overload Torque

Cited by 18 publications

References 21 publications

Vision, Challenges, and Future Trends of Model Predictive Control in Switched Reluctance Motor Drives

Vision, Challenges, and Future Trends of Model Predictive Control in Switched Reluctance Motor Drives

Recent Achievements in Model Predictive Control Techniques for Industrial Motor: A Comprehensive State-of-the-Art

Minimization of torque ripple in switched reluctance motor based on MPC and TSF

Contact Info

Product

Resources

About