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Showing 2 results for Interior Permanent Magnet Synchronous Motor

K. Malekian, J. Milimonfared, B. Majidi,
Volume 5, Issue 1 (3-2009)
Abstract

The main theme of this paper is to present novel controller, which is a genetic based fuzzy Logic controller, for interior permanent magnet synchronous motor drives with direct torque control. A radial basis function network has been used for online tuning of the genetic based fuzzy logic controller. Initially different operating conditions are obtained based on motor dynamics incorporating uncertainties. At each operating condition, a genetic algorithm is used to optimize fuzzy logic parameters in closed-loop direct torque control scheme. In other words, the genetic algorithm finds optimum input and output scaling factors and optimum number of membership functions. This optimization procedure is utilized to obtain the minimum speed deviation, minimum settling time, zero steady-state error. The control scheme has been verified by simulation tests with a prototype interior permanent magnet synchronous motor.
S. Ahmadi, A. Vahedi,
Volume 11, Issue 3 (9-2015)
Abstract

In this paper a multiobjective optimal design method of interior permanent magnet synchronous motor ( IPMSM) for traction applications so as to maximize average torque and to minimize torque ripple has been presented. Based on train motion equations and physical properties of train, desired specifications such as steady state speed, rated output power, acceleration time and rated speed of traction motor are related to each other. By considering the same output power, steady state speed, rated voltage, rated current and different acceleration time for a specified train, multiobjective optimal design has been performed by Broyden–Fletcher–Goldfarb–Shanno (BFGS) method and finite element method (FEM) has been chosen as an analysis tool. BFGS method is one of Quasi Newton methods and is counted in classic approaches. Classic optimization methods are appropriate when FEM is applied as an analysis tool and objective function isn’t expressed in closed form in terms of optimization variables.

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