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Article
PSO-Based EKF Estimator Design for PMBLDC Motor

Authors: Ahmed Hammood Abed --- Mohammed Moanes E. Ali
Journal: Engineering and Technology Journal مجلة الهندسة والتكنولوجيا ISSN: 16816900 24120758 Year: 2016 Volume: 34 Issue: 8 Part (A) Engineering Pages: 1651-1665
Publisher: University of Technology الجامعة التكنولوجية

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Abstract

The estimation of motor state variables is an important criterion in the drive performance, especially for high accuracy required, for that reason it’s-necessary to estimate rotor-position-continuously not for sixty-electrical-degrees as in most existing methods. In this work the speed and position for the rotor of Permanent Magnet Brushless DC Motor (PMBLDCM) was estimated by using extended Kalman filter (EKF), this work is divided into two parts, the first one deals with design and simulation of PMBLDCM with EKF as an estimator, the results are introduced by manually selected EKF parameters (Q & R) matrices, The second one deals with investigation the performance of the use of PSO technique to optimize the performance and operation of EKF, the main use of PSO here is to optimize value for EKF parameters (Q and R), the results proved that by tuning the EKF parameters by PSO the estimated values for speed and position is very-close-to the actual value-(estimation-accuracy is increased). The resultant error clearly decreases when tuned by EKF parameters for example at full load case the speed RMS error is 0.24 for 10μs sampling time, although the RMS error is 9 for 10μs sampling time trial and error selected EKF parameters.


Article
Comparing Kalman Filter and Dynamic Adaptive Neuro Fuzzy for Integrating of INS/GPS Systems
مقارنة كالمان فلتروديناميكية الشبكةالمكيفةالمضببة لتكامل منظومتي INS/GPS

Authors: Sameir A. Aziez --- Huda Naji Abdul-Rihda
Journal: Engineering and Technology Journal مجلة الهندسة والتكنولوجيا ISSN: 16816900 24120758 Year: 2016 Volume: 34 Issue: 1 Part (A) Engineering Pages: 61-72
Publisher: University of Technology الجامعة التكنولوجية

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Abstract

Global Positioning System (GPS) and Inertial Navigation System (INS) technologies have been widely used in a variety of positioning and navigation applications. Both Systems have their unique features and shortcomings. Hence, combined system of GPS and INS can exhibit the robustness, higher bandwidth and better noise characteristics of the inertial system with the long-term stability of GPS, Integrated together are used to provide a reliable Navigation System. This paperwill compare the performance of Kalman filter and Dynamic adaptive neuro fuzzy system for integrated INS/GPS systems. The Simulation Results by Matlab7 Programming Language showed great improvements in positioning, gives a best results and reduce the root mean square error (r.m.s.) when used Dynamic adaptive neuro fuzzy system rather than Kalman filter.

منظومةتحديدالموقع العالمي (GPS) ومنظومةالملاحة ذاتالقصور الذاتي(INS) تستخدم بشكل واسع في مختلف تطبيقات تحديد المواقع والملاحة .كلا المنظومتين لها ميزات فريدة وعيوب . بتكامل المنظومتين (INS& GPS) نستطيع الحصول على نظام ملاحة متين, نطاق ترددي عالي , وخصائص ضوضاء أفضل بالنسبة لمنظومة (INS) واستقرارية عالية لمنظومة.(GPS)لذلك ,تكامل المنظومتين يستخدم للحصول على نظام ملاحة ذات وثوقية عالية. في هذا البحث تمت المقارنة بين أداء كالمان فلتروديناميكية الشبكةالمكيفةالمضببة لتكامل المنظومتين. (باستخدام لغة البرمجة Matlab7) حيث تبين تحسينات كبيرة في تحديد المواقع وان r.m.s للخطاء اقل بكثير باستخدام الشبكة المكيفة المضببة بمقارنتها مع نتائج كالمان فلتر .

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