Robust Model Predictive Control of Permanent Magnet Synchronous Motor Under Parameter Mismatch covers permanent magnet synchronous motors (PMSMs), an essential element of electromechanical systems like urban rail vehicles and wind power generation thanks to their fast torque response and wide speed range. This book presents improved MPC methods to enhance robustness against parameter mismatches. It aims to help researchers understand advanced motor control strategies and assist engineers in effectively applying these algorithms to improve the operational performance of industrial motor systems.
Model predictive control (MPC) offers high accuracy and dynamic response, making it suitable for PMSMs. However, MPC relies on accurate motor parameters, which may vary over time, leading to mismatches that affect performance. These mismatches can cause unstable switching frequencies and increased torque/flux ripples, becoming a common fault in PMSM systems.
- Provides greater understanding of sensitivity analysis and predictive control of permanent magnet synchronous motors with parameter mismatches
- Increases opportunities to conduct research in this field by providing model predictive control methods and code
- Offers the latest developments in model predictive control methods of permanent magnet synchronous motors
- Presents a technical book for engineers in industrial motor applications
Gongping Wu received the Ph.D. degree in electrical engineering from the College of Electrical and Information Engineering, Hunan University, Changsha, China, in 2021. He studied with College of Electronic and Information Engineering, Tongji University, Shanghai, China, from September 2011 to September 2013. From 2019 to 2021, he was a Visiting student with the Department of Electrical Engineering, Technical University of Denmark, Lyngby, Denmark. He is an Associate Professor with the College of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha, China. His current research interests include motor fault diagnosis, model predictive control, and permanent magnet synchronous motor systems.
Sheng Huang received the M.S. and Ph.D. degrees in electrical engineering from the College of Electrical and Information Engineering, Hunan University, Changsha, China, in 2012 and 2016, respectively. Since 2023, he has been a Professor with College of Electrical and Information Engineering, Hunan University. His research interests include renewable energy generation, modeling and integration study of wind power, and permanent magnet synchronous motor systems.
Qiuwei Wu received the PhD degree in Electrical Engineering from Nanyang Technological University, Singapore, in 2009. He is a professor with the School of Electronics, Electrical Engineering, and Computer Science (EEECS), Queen’s University Belfast, the UK. His research interests are distributed optimal operation and control of low carbon power and energy systems, including distributed optimal control of wind power, optimal operation of active distribution networks, and optimal operation and planning of integrated energy systems.
Vladimir Terzija is a Full Professor at the School of Engineering, Newcastle University, in the United Kingdom. He was previously a Full Professor and the Head of Laboratory of Modern Energy Systems with Skoltech, Russian Federation, EPSRC Chair Professor in power system engineering at the University of Manchester, UK, and an Assistant Professor at the University of Belgrade. Additionally, he was a Senior Specialist for switchgear and distribution automation with ABB, in Germany. His research interests include smart grid applications, wide-area monitoring, protection and control, multi-energy systems, switchgear and transient processes, ICT, data analytics, and digital signal processing applications in power systems. Prof. Terzija was the recipient of the prestigious Alexander von Humboldt Fellowship, and is the Editor-in-Chief of the International Journal of Electrical Power and Energy Systems.
