نمذجة ومحاكاة المتحكم الضبابي ذاتي التنظيم باستخدام MATLAB & Simulink
Abstract
منذ أن تم اختراع المنطق الضبابي والتحكم الضبابي حظي الأخير بانتشار واهتمام متزايدين في تطبيقات متنوعة وفي الأجهزة المختلفة في مختلف نواحي الحياة. ولعل ذلك لسهولة تطبيق نظام التحكم الضبابي ولابتعاده غالباً عن تعقيدات العلاقات الرياضية. حتى لو لم نكن نعلم نموذج النظام موضوع التحكم، يستطيع المتحكم الضبابي ذاتي التنظيم (SOFC) تحسين استجابة متحكم ضبابي خطي موجود أو بناء جدول تحكم من الصفر، من خلال تقييم أداء المتحكم الحالي وتعديل جدول التحكم بناءً على ذلك. يقدم هذا البحث مادة بسيطة توضح كيفية تصميم واستخدام المتحكم الضبابي ذاتي التنظيم. من خلال عملية نمذجة ومحاكاة باستخدام Matlab & Simulink® حيث تم فيها استخدام المتحكم لتنظيم سرعة محرك كهربائي مستمر عند حمولات متغيرة. وأظهرت المحاكاة قدرة المتحكم على تقديم استجابة جيدة وتقليل خطأ السرعة بشكل ملحوظ عند تغير الحمولة. يعد البحث مادة نصية يمكن أن يرجع إليها طلبتنا والباحثون المهتمون في مجال التحكم التكيفي عامة، والتحكم الضبابي ذاتي التنظيم خاصة.
Since the invention of Fuzzy logic and fuzzy control, the latter has been growing in spread and importance in many applications and devices in many life aspects. This maybe due to the easy use of a fuzzy control system, and for being far of math complications. Even if the plant model is unknown, a self-organizing fuzzy controller (SOFC) can improve the response of an already exist linear control table, or even can build a control table from scratch, by assessing current performance of the controller and adjusting the control table accordingly. This paper provides a simple article that shows how to design and use a self-organizing fuzzy controller, through a simulation example using MATLAB & Simulink® in which a variable torque loaded DC motor speed regulation is done. The simulation showed the ability of the controller to provide a good response and decrease speed error by a notable amount at load torque changing times. This paper can be used as textbook material for students or researchers interested in the field of adaptive control, especially self-organizing fuzzy control.
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