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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) International Journal of Advances in Applied Sciences
Rizauddin Ramli
Universiti Kebangsaan Malaysia
Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Optimal parameter estimation for a DC motor using genetic algorithm Mohammad Soleimani Amiri; Mohd Faisal Ibrahim; Rizauddin Ramli
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 11, No 2: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (717.047 KB) | DOI: 10.11591/ijpeds.v11.i2.pp1047-1054

Abstract

Estimating the parameters of a geared DC motor is crucial in terms of its non-linear features. In this paper, parameters of a geared DC motor are estimated genetically. Mathematical model of the DC motor is determined by Kirchhoff’s law and dynamic model of its shafts and gearbox. Parameters of the geared DC motor are initially estimated by MATLAB/SIMULINK. The estimated parameters are defined as initial values for Genetic Algorithm (GA) to minimize the error of the simulated and actual angular trajectory captured by an encoder. The optimal estimated model of the geared DC motor is validated by different voltages as the input of the actual DC motor and its mathematical model. The results and numerical analysis illustrate it can be ascertained that GA is appropriate to estimate the parameters of platforms with non linear characteristics.
Theory and development of magnetic flux leakage sensor for flaws detection: A review Nor Afandi Sharif; Rizauddin Ramli; Abdullah Zawawi Mohamed; Mohd Zaki Nuawi
International Journal of Advances in Applied Sciences Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (793.986 KB) | DOI: 10.11591/ijaas.v8.i3.pp208-216

Abstract

This paper presents a review of state-of-art in the Magnetic Flux Leakage (MFL) sensor technology, which plays an important role in Nondestructive Testing (NDT) to detect crack and corrosion in ferromagnetic material. The demand of more reliable MFL tools and signal acquisition increase as it has a direct impact on structure integrity and can lead to be major catastrophic upon questionable signal analysis. This is because the size, cost, efficiency, and reliability of the extensive MFL system for NDT applications primarily depend on signal acquisition as a qualitative measure in producing a trustworthy analysis. Therefore, the selection of appropriate tools and methodology plays a major role in determining the comprehensive performance of the system. This paper also reviews an Artificial Neural Network (ANN) and Finite Element Method (FEM) in developing an optimum permeability standard on the test piece.  
Co-Authors Abdullah Zawawi Mohamed Mohammad Soleimani Amiri Mohd Faisal Ibrahim Mohd Zaki Nuawi Nor Afandi Sharif