Название: Model Predictive Control for AC Motors: Robustness and Accuracy Improvement TechniquesАвтор: Yaofei Han, Chao Gong, Jinqiu Gao
Издательство: Springer
Год: 2022
Страниц: 137
Язык: английский
Формат: pdf (true)
Размер: 17.5 MB
This book introduces how to improve the accuracy and robustness of model predictive control (MPC). 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.
MPC has been becoming increasingly popular in the field of AC motor control, but there are still many problems influencing the control performance. The authors will continuously focus on improving the robustness and accuracy of the MPC methods in the future.
The most commonly used AC motors in engineering include induction motors (IM) and permanent magnet synchronous motors (PMSM). Besides, the wound field synchronous motors (WFSM) are witnessing a rising trend due to the novel excitation structures. This book treats these three kinds of motors as the research objects. More than one MPC strategies are focused on in this book, which include finite control set MPC (FCS-MPC), continuous control set MPC (CCS-MPC), model predictive current control (MPCC), model predictive speed control (MPSC) and multi-objective MPC. This book clearly introduces the up-to-date MPC methods developed by the authors in recent years.
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