Parameter Estimation of Permanent Magnet Synchronous Machines (IEEE Press Series on Control Systems Theory and Applications) - Hardcover

Zhu, Zi Qiang; Liu, Kan; Liang, Dawei

Hardcover

ISBN 10: 1394280424 ISBN 13: 9781394280421

Publisher: Wiley-IEEE Press, 2025

Synopsis

Comprehensive reference delivering basic principles and state-of-the-art parameter estimation techniques for permanent magnet synchronous machines (PMSMs)

Parameter Estimation of Permanent Magnet Synchronous Machines reviews estimation techniques of the parameters of PMSMs, introducing basic models and techniques, as well as issues and solutions in parameter estimation challenges, including rank deficiency, inverter nonlinearity, and magnetic saturation. This book is supported by theories, experiments, and simulation examples for each technique covered.

Topics explored in this book include:

• Electrical and mechanical parameter estimation techniques, including those based on current/voltage injection and position offset injection, under constant or variable speed and load for sensored or sensorless controlled PMSMs, accounting for magnetic saturation, cross-coupling, inverter nonlinearity, temperature effects, and more
• Recursive least squares, the Kalman filter, model reference adaptive systems, Adaline neural networks, gradient-based methods, particle swarm optimization, and genetic algorithms
• Applications of parameter estimation techniques for improvement of control performance, sensorless control, thermal condition monitoring, and fault diagnosis

This book is an essential reference for professionals working on the control and design of electrical machines, researchers studying electric vehicles, wind power generators, aerospace, industrial drives, automation systems, robots, and domestic appliances, as well as advanced undergraduate and graduate students in related programs of study.

About this title

About the Author

Zi Qiang Zhu is a Fellow of the Royal Academy of Engineering and the Head of the Electrical Machines and Power Research Group at the University of Sheffield, UK.

Kan Liu is the Assistant Dean of the College of Mechanical and Vehicle Engineering at Hunan University, China.

Dawei Liang is a Postdoctoral Research Associate with the University of Sheffield, UK.

From the Back Cover

Comprehensive reference delivering basic principles and state-of-the-art parameter estimation techniques for permanent magnet synchronous machines (PMSMs)

Parameter Estimation of Permanent Magnet Synchronous Machines reviews estimation techniques of the parameters of PMSMs, introducing basic models and techniques, as well as issues and solutions in parameter estimation challenges, including rank deficiency, inverter nonlinearity, and magnetic saturation. This book is supported by theories, experiments, and simulation examples for each technique covered.

Topics explored in this book include:

• Electrical and mechanical parameter estimation techniques, including those based on current/voltage injection and position offset injection, under constant or variable speed and load for sensored or sensorless controlled PMSMs, accounting for magnetic saturation, cross-coupling, inverter nonlinearity, temperature effects, and more
• Recursive least squares, the Kalman filter, model reference adaptive systems, Adaline neural networks, gradient-based methods, particle swarm optimization, and genetic algorithms
• Applications of parameter estimation techniques for improvement of control performance, sensorless control, thermal condition monitoring, and fault diagnosis

This book is an essential reference for professionals working on the control and design of electrical machines, researchers studying electric vehicles, wind power generators, aerospace, industrial drives, automation systems, robots, and domestic appliances, as well as advanced undergraduate and graduate students in related programs of study.

Edition details

• Publisher: Wiley-IEEE Press
• Publication date: 2025
• Language: English
• ISBN 10: 1394280424
• ISBN 13: 9781394280421
• Binding: Hardcover
• Edition number: 1
• Number of pages: 288