14 Nov 2018
Prestigious award for DPhil work improving lifespan and performance of electrical motors
Paper wins 1st prize from journal IEEE Transactions on Power Electronics
![Optimization researcher Bartolomeo Stellato receiving first prize from the journal IEEE Transactions on Power Electronics for a paper titled ‘High-Speed Finite Control Set Model Predictive Control for Power Electronics’](/media/1412/bartolomeo-stellato.jpg?width=850&height=450&format=webp&quality=85&v=1d9e1da238d6750)
Optimization researcher Bartolomeo Stellato received first prize from the journal IEEE Transactions on Power Electronics for a paper titled ‘High-Speed Finite Control Set Model Predictive Control for Power Electronics’. The award comes with a cash prize of $1,000.
"It’s an honour to receive such an important prize. I appreciate the power electronics community’s interest in our work, and more generally in novel approaches from embedded optimization.
The paper, which details the team’s work on improving the lifespan and performance of electrical motors, was written under the supervision of Professor Paul Goulart, Associate Professor at the Department’s Control Group and in collaboration with Tobias Geyer from ABB Switzerland. They built a physical model to predict how these devices behave and to speed up the computing process, which led to an efficient algorithm that details the motor’s performance.
The journal’s prizes reward excellence in the papers they receive, with a specific emphasis on originality, contribution to the field, the extent to which the paper is supported by analysis and experimental evidence, and quality of presentation.
Bartolomeo’s DPhil work was undertaken as part of the TEMPO (Training in Embedded Predictive Control and Optimization) project. Part of the EU Marie Curie framework, the TEMPO programme identifies highly motivated young scientists, combining state-of-the-art research with a comprehensive training program.
Now based at the MIT Sloan School of Management, the business school of the Massachusetts Institute of Technology, Bartolomeo is working at the intersection of Machine Learning and Mathematical Optimization.
“I believe this intersection of these two fields can bring many more research breakthroughs, and I am deeply thankful to my co-authors for their constant support throughout this project.”
Bartolomeo’s paper can be found online at the website of the IEEE Transactions on Power Electronics.
Video
Bartolomeo and fellow Engineering Science postgraduate student Goran Banjac discuss their involvement with the TEMPO project.