Analysis and design of a Variable Flux Memory Motor for a humanoid robot application
Abstract
The variable flux memory motor (VFMM) is a relatively new class of machine that affords one the ability to actively change a motor from a high torque/low speed device into a low torque/high speed device through the online control of the rotor permanent magnet field strength. In this research, tools for the design of a population-based design (PBD) of a VFMM are developed. The primary tool is a magnetic equivalent circuit (MEC) model that is used to calculate the machine inductances, the magnetic flux linkage, and core losses based upon machine geometry and material properties. The MEC is solved using a mesh based approach that has been shown to have numerical advantage over a nodal based solution. The MEC model is coupled to an optimization engine that is configured to perform multi-objective optimization (MOO) using evolutionary based strategies. Design study tools are applied to establish the trade off between mass and efficiency for a humanoid robot application. Finally, the Pareto-Optimal front is investigated and candidate designs are selected for validation using a finite element analysis.
Degree
M.S.E.C.E.
Advisors
Pekarek, Purdue University.
Subject Area
Electrical engineering|Electromagnetics|Robotics|Energy
Off-Campus Purdue Users:
To access this dissertation, please log in to our
proxy server.