Magnetic Bearing Rotordynamic System Optimization Using Multi-Objective Genetic Algorithms-Reference-Cited by-同舟云学术

Magnetic Bearing Rotordynamic System Optimization Using Multi-Objective Genetic Algorithms

Published:2014-09-24 Issue:2 Volume:137 Page:
ISSN:0022-0434
Container-title:Journal of Dynamic Systems, Measurement, and Control
language:en
Short-container-title:

Author:

Zhong Wan¹,Palazzolo Alan²

Affiliation:

1. Mechanical Engineering, Texas A&M University, College Station, TX 77843 e-mail:

2. ASME Fellow Professor Mechanical Engineering, Texas A&M University, College Station, TX 77843 e-mail:

Abstract

Multiple objective genetic algorithms (MOGAs) simultaneously optimize a control law and geometrical features of a set of homopolar magnetic bearings (HOMB) supporting a generic flexible, spinning shaft. The minimization objectives include shaft dynamic response (vibration), actuator mass and total actuator power losses. Levitation of the spinning rotor and dynamic stability are constraint conditions for the control law search. Nonlinearities include magnetic flux saturation, and current and voltage limits. Pareto frontiers were applied to identify the best-compromised solution. Mass and vibration reductions improve with a two control law approach.

Publisher

ASME International

Subject

Computer Science Applications,Mechanical Engineering,Instrumentation,Information Systems,Control and Systems Engineering

Link

http://asmedigitalcollection.asme.org/dynamicsystems/article-pdf/doi/10.1115/1.4028401/6118000/ds_137_02_021012.pdf

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