Model Predictive Control for AC Motors: Robustness and Accuracy Improvement Techniques
Editat de Yaofei Han, Chao Gong, Jinqiu Gaoen Limba Engleză Hardback – 9 feb 2022
Chapter 2 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com
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Specificații
ISBN-13: 9789811680656
ISBN-10: 9811680655
Pagini: 130
Ilustrații: XI, 129 p. 78 illus., 76 illus. in color.
Dimensiuni: 155 x 235 x 17 mm
Greutate: 0.38 kg
Ediția:1st ed. 2022
Editura: Springer Nature Singapore
Colecția Springer
Locul publicării:Singapore, Singapore
ISBN-10: 9811680655
Pagini: 130
Ilustrații: XI, 129 p. 78 illus., 76 illus. in color.
Dimensiuni: 155 x 235 x 17 mm
Greutate: 0.38 kg
Ediția:1st ed. 2022
Editura: Springer Nature Singapore
Colecția Springer
Locul publicării:Singapore, Singapore
Cuprins
Model Predictive Control Principles For AC Motors.- Robustness against Stator Parameter Mismatch.- Robustness against Rotor Parameter Mismatch.- Accuracy Improvement of Model Predictive Control.
Notă biografică
Chao Gong has been studying model predictive control and permanent magnet synchronous machines since 2014, which are the main contents of this book. He is going to be a tenure-track professor with the School of Automation, Northwestern Polytechnical University this year.
Until now, He has published a total of twenty-two journal and international conference papers, cited by 219 times. Besides, He has been granted eleven invention patents in the fields of machine design/control. He has participated in two Chinese province-level projects and the UK Newton Advanced Fellowship project which are related to permanent magnet machines. Now, He is the reviewer for four top journal papers, which include IEEE Transactions on Industrial Electronics, IEEE Transactions on Power Electronics, IEEE on Industrial Informatics, IEEE Access. He won the Best Poster Presentation at a conference organized by IEEE UK and Ireland Power Electronics Chapter in March 2019 and was selected to present a Poster at the STEM for Britain Exhibition in the Engineering Section held at the House of Commons in March 2019. His achievements have been widely recognized by the peers. He won the KM Stott Prize for excellence in research and IET Postgraduate Prize in 2021.
Yaofei Han (S’07-M’17) was born in Henan, China. He received the M.S. in 2005 and his Ph.D. in 2010, in power electronics and drives from China University of Mining and Technology respectively. He had been an associate professor at Henan University of Urban Construction since 2012, served in this capacity from 2010 to 2019. He was with the School of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University (VT), as a visiting scholar from 2017 to 2019. He is currently an Associate Professor of power electronics and electrical drives at National Maglev Transportation Engineering R&D Center, Tongji University, Shanghai, China. His research interests includemulti-level power converter for power conversion and motor control, high-efficiency converter for renewable power conversion system.
Ms. Jinqiu Gao was born in Shannxi province in P.R. China, on January 7, 1996. She received the Bachelor and master degrees in electrical engineering from Northwestern Polytechnical University, Xi'an, China, in 2017 and 2020, respectively. She is currently working toward the Ph.D. degree in control science and engineering with the Central South University, Changsha, China. Her research interests include fault diagnosis for traction motor, power electronics and motion control
Ms. Jinqiu Gao was born in Shannxi province in P.R. China, on January 7, 1996. She received the Bachelor and master degrees in electrical engineering from Northwestern Polytechnical University, Xi'an, China, in 2017 and 2020, respectively. She is currently working toward the Ph.D. degree in control science and engineering with the Central South University, Changsha, China. Her research interests include fault diagnosis for traction motor, power electronics and motion control
Textul de pe ultima copertă
This book introduces how to improve the accuracy and robustness of model predictive control. Firstly, the disturbance observation- and compensation-based method is developed. Secondly, direct parameter identification methods are developed. Thirdly, the seldom-focused-on issues such as sampling and delay problems are solved in this book. Overall, this book solves the problems in a systematic and innovative way.
Chapter 2 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com
Chapter 2 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com
Caracteristici
Includes in-depth discussions on accuracy and robustness of model predictive control Covers special topics illustrating the accuracy of model predictive control Includes systematical approaches solving the parameter mismatch issue