Bai / Lee Permanent Magnet Spherical Motors

Model and Field Based Approaches for Design, Sensing and Control
1. Auflage 2018
ISBN: 978-981-10-7962-7
Verlag: Springer Nature Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 170 Seiten

Reihe: Research on Intelligent Manufacturing

This book introduces and illustrates modeling, sensing, and control methods for analyzing, designing, and developing spherical motors. It systematically presents models for establishing the relationships among the magnetic fields, position/orientation and force/torque, while also providing time-efficient solutions to assist researchers and engineers in studying and developing these motors. In order to take full advantage of spherical motors' compact structure in practical applications, sensing and control methods that utilize their magnetic fields and eliminate the need to install external sensors for feedback are proposed. Further, the book investigates for the first time spherical motors' force/torque manipulation capability, and proposes algorithms enabling the ball-joint-like end-effector for haptic use based on these motors' hybrid position/force actuation modes. While systematically presenting approaches to their design, sensing and control, the book also provides many examples illustrating the implementation issues readers may encounter.  



Kun Bai: Professor Kun Bai received his B.S. degree from Zhejiang University, China in 2006 and earned his M. S. and Ph. D. degrees from the Woodruff School of Mechanical Engineering at Georgia Institute of Technology, Atlanta, US in 2009 and 2012 respectively. Currently, he is an Associate Professor with the State Key Laboratory of Digital Manufacturing Equipment and Technology and the School of Mechanical Science and Engineering at Huazhong University of Science and Technology, China. Prof. Bai's research areas include smart electromagnetic actuators/sensors and novel applications, in which he has published over 20 peer-viewed papers and held over 10 international and domestic patents. He has extensive expertise and experience in developing direct drive electromagnetic actuators. He has been PI for several funded projects regarding manufacturing and robotics where spherical motors has been developed for applications such as conformal printing, haptic device, desktop machining-stage. Kok-Meng Lee: Professor Kok-Meng Lee earned his B.S. degree from the University of Buffalo, the State University of New York, Buffalo, NY, USA, in 1980, and M. S. and Ph. D. degrees from Massachusetts Institute of Technology, Cambridge, MA, USA, in 1982 and 1985, respectively. He is currently Professor of Mechanical Engineering at Georgia Institute of Technology, Atlanta, GA, USA. He is also Distinguished Professor with the State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, China, under Thousand Talents Plan. Prof. Lee's research interests include system dynamics/control, robotics, automation, and mechatronics. He is a world renowned researcher with more than 30 years of research experience in magnetic field modeling and design, optimization and implementation of electromagnetic actuators. He has published over 150 peer-reviewed papers and he holds eight patents in machine vision, three degrees of freedom (DOF) spherical motor/encoder, and live-bird handling system. He is IEEE/ASME Fellow and was the Editor-in-Chief for the IEEE/ASME Transactions on Mechatronics from 2008 to 2013. Recognitions of his research contributions include the National Science Foundation (NSF) Presidential Young Investigator, Sigma Xi Junior Faculty Research, International Hall of Fame New Technology, and Kayamori Best Paper awards. 

Autoren/Hrsg.

Bai, Kun

Lee, Kok-Meng

Inhaltsverzeichnis

1;Preface;6
2;Contents;8
3;Nomenclature;11
4;1 Introduction;13
4.1;1.1 Background;13
4.2;1.2 The State of the Art;15
4.2.1;1.2.1 Magnetic Modeling and Analysis;18
4.2.2;1.2.2 Orientation Sensing;20
4.2.3;1.2.3 Control Methods;22
4.3;1.3 Book Outline;24
4.4;References;26
5;Modeling Methods;30
6;2 General Formulation of PMSMs;31
6.1;2.1 PMSM Electromagnetic System Modeling;31
6.1.1;2.1.1 Governing Equations of Electromagnetic Field;31
6.1.2;2.1.2 Boundary Conditions;34
6.1.3;2.1.3 Magnetic Flux Linkage and Energy;35
6.1.4;2.1.4 Magnetic Force/Torque;36
6.2;2.2 PMSM Rotor Dynamics;37
6.3;References;40
7;3 Distributed Multi-pole Models;41
7.1;3.1 Distributed Multi-pole Model for PMs;41
7.1.1;3.1.1 PM Field with DMP Model;42
7.1.2;3.1.2 Numerical Illustrative Examples;45
7.2;3.2 Distributed Multi-pole Model for EMs;53
7.2.1;3.2.1 Equivalent Magnetization of the ePM;55
7.2.2;3.2.2 Illustrations of Magnetic Field Computation;57
7.3;3.3 Dipole Force/Torque Model;57
7.3.1;3.3.1 Force and Torque on a Magnetic Dipole;57
7.3.2;3.3.2 Illustration of Magnetic Force Computation;59
7.4;3.4 Image Method with DMP Models;62
7.4.1;3.4.1 Image Method with Spherical Grounded Boundary;63
7.4.2;3.4.2 Illustrative Examples;66
7.4.3;3.4.3 Effects of Iron Boundary on the Torque;68
7.5;3.5 Illustrative Numerical Simulations for PMSM Design;72
7.5.1;3.5.1 Pole Pair Design;75
7.5.2;3.5.2 Static Loading Investigation;80
7.5.3;3.5.3 Weight-Compensating Regulator;81
7.6;Appendix;85
7.7;References;89
8;4 PMSM Force/Torque Model for Real-Time Control;91
8.1;4.1 Force/Torque Formulation;91
8.1.1;4.1.1 Magnetic Force/Torque Based on the Kernel Functions;92
8.1.2;4.1.2 Simplified Model: Axis-Symmetric EMs/PMs;95
8.1.3;4.1.3 Inverse Torque Model;96
8.2;4.2 Numerical Illustrations;96
8.2.1;4.2.1 Axis-Asymmetric EM/PMs;96
8.2.2;4.2.2 Axis-Symmetric EM/PM;100
8.3;4.3 Illustrative PMSM Torque Modelling;103
9;Sensing Methods;106
10;5 Field-Based Orientation Sensing;107
10.1;5.1 Coordinate Systems and Sensor Placement;107
10.2;5.2 Field Mapping and Segmentation;108
10.3;5.3 Artificial Neural Network Inverse Map;110
10.4;5.4 Experimental Investigation;111
10.4.1;5.4.1 2-DOF Concurrent Characterization;112
10.5;References;115
11;6 A Back-EMF Method for Multi-DOF Motion Detection;116
11.1;6.1 Back-EMF for Multi-DOF Motion Sensing;116
11.1.1;6.1.1 EMF Model in a Single EM-PM Pair;118
11.1.2;6.1.2 Back-EMF with Multiple EM-PM Pairs;119
11.2;6.2 Implementation of Back-EMF Method on a PMSM;121
11.2.1;6.2.1 Mechanical and Magnetic Structure of the PMSM;122
11.2.2;6.2.2 Numerical Solutions for the MFL Model;123
11.2.3;6.2.3 Experiment and Discussion;125
11.2.4;6.2.4 Parameter Estimation of the PMSM with Back-EMF Method;127
11.3;Appendix;129
11.4;References;129
12;Control Methods;130
13;7 Direct Field-Feedback Control;131
13.1;7.1 Traditional Orientation Control Method for Spherical Motors;131
13.1.1;7.1.1 PD Control Law and Stability Analysis;132
13.1.2;7.1.2 Comments on Implementation of Traditional Control Methods;133
13.2;7.2 Direct Field-Feedback Control;134
13.2.1;7.2.1 Determination of Bijective Domain;135
13.2.2;7.2.2 DFC Control Law and Control Parameter Determination;135
13.2.3;7.2.3 DFC with Multi-sensors;136
13.3;7.3 Numerical 1-DOF Illustrative Example;137
13.3.1;7.3.1 Sensor Design and Bijective Domain Identification;137
13.3.2;7.3.2 Field-Based Control Law;139
13.3.3;7.3.3 Numerical Illustrations of Multiple Bijective Domains;141
13.4;7.4 Experimental Investigation of DFC for 3-DOF PMSM;141
13.4.1;7.4.1 System Description;141
13.4.2;7.4.2 Sensor Design and Bijective Domains;144
13.4.3;7.4.3 Bijective Domain;145
13.4.4;7.4.4 TCV Computation Using Artificial Neural Network (ANN);148
13.4.5;7.4.5 Experimental Investigation;148
13.5;Appendix;156
13.6;References;156
14;8 A Two-Mode PMSM for Haptic Applications;157
14.1;8.1 Description of the PMSM Haptic Device;157
14.1.1;8.1.1 Two-Mode Configuration Design for 6-DOF Manipulation;159
14.1.2;8.1.2 Numerical Model for Magnetic Field/Torque Computation;160
14.1.3;8.1.3 Field-Based TCV Estimation;161
14.2;8.2 Snap-Fit Simulation;162
14.2.1;8.2.1 Snap-Fit Performance Analyses;164
14.2.2;8.2.2 Snap-Fit Haptic Application;165
14.3;Appendix: PM/EM/Sensor Position Coordinates;169
14.4;References;170