Open AccessSpeed Control of PMDCM Based GA and DS Techniques
Author(s) -
Wisam Najm Al-Din Abed,
Adham Hadi Saleh,
Abbas Salman Hameed
Publication year - 2018
Publication title -
international journal of power electronics and drive systems (ijpeds)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.322
H-Index - 21
ISSN - 2088-8694
DOI - 10.11591/ijpeds.v9.i4.pp1467-1475
Subject(s) - computer science , transient (computer programming) , controller (irrigation) , matlab , approximation error , genetic algorithm , mean squared error , control theory (sociology) , process (computing) , software , performance improvement , algorithm , simulation , artificial intelligence , control (management) , mathematics , machine learning , statistics , engineering , operations management , agronomy , biology , programming language , operating system
Permanent magnet direct current motors (PMDCM) are widely used in various applications such as space technologies, personal computers, medical, military, robotics, electrical vehicles, etc. In this paper, the mathematical model of PMDCM is designed and simulated using MATLAB software. The PMDCM speed is controlled using rate feedback controller due to its ability of improving system damping. To improve the controller performance, it’s parameters are tuned using genetic algorithm (GA) and direct search (DS) techniques. The tuning process based on different performance criteria. The most four common performance criteria used in this paper are JIAE (Integral of Absolute Error), JISE (Integral of Square Error), JITAE (Integral of Time-Weighted Absolute Error), and JITSE (Integral of Time-Weighted Square Error). The results obtained from these evolutionary techniques are compared. The results show an obvious improvement in system performance including enhancing the transient and steady state of PMDCM speed responses for all performance criteria.