S
Shamsuddeen Nalakath
Researcher at McMaster University
Publications - 32
Citations - 574
Shamsuddeen Nalakath is an academic researcher from McMaster University. The author has contributed to research in topics: Model predictive control & Inverter. The author has an hindex of 8, co-authored 32 publications receiving 243 citations. Previous affiliations of Shamsuddeen Nalakath include TVS Motor Company & McMaster-Carr.
Papers
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Journal ArticleDOI
Thermal management of electric machines
Yinye Yang,Berker Bilgin,Michael Kasprzak,Shamsuddeen Nalakath,Hossam Sadek,Matthias Preindl,James S. Cotton,Nigel Schofield,Ali Emadi +8 more
TL;DR: In this article, the authors present various important aspects of thermal management in electric machines with the main focus on transportation applications and discuss design considerations, challenges, and methods for enhanced thermal management.
Journal ArticleDOI
Optimization-Based Position Sensorless Finite Control Set Model Predictive Control for IPMSMs
TL;DR: This paper presents nonlinear optimization-based position and speed estimation scheme for IPMSM drives with arbitrary signal injection generated by inherent switching ripples associated with finite control set model predictive control and proposes a compensator for standstill operation that prevents converging to saddle and symmetrical solutions.
Journal ArticleDOI
Universal Full-Speed Sensorless Control Scheme for Interior Permanent Magnet Synchronous Motors
Dianxun Xiao,Shamsuddeen Nalakath,Silvio Rotilli Filho,Gaoliang Fang,Alice Dong,Yingguang Sun,Jason Wiseman,Ali Emadi +7 more
TL;DR: A unified algorithm is designed to achieve arbitrary injection for low-speed sensorless control and seamless transition across full-speed region and the feasibility of the proposed estimation algorithm is verified for a 15-kW IPMSM drive.
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A New Torque Sharing Function Method for Switched Reluctance Machines With Lower Current Tracking Error
TL;DR: A new torque sharing function (TSF) method is proposed for torque ripple reduction in switched reluctance machines (SRMs) and exhibits lower current tracking error due to the consideration of the current dynamics.
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Online Optimal Tracking Method for Interior Permanent Magnet Machines With Improved MTPA and MTPV in Whole Speed and Torque Ranges
TL;DR: The proposed online optimal tracking method is successfully implemented in an off-the-shelf motor control unit, and the performances are experimentally validated for wide operating regions.