طراحی و پیاده‌سازی یک کنترل‌کننده بهینه‌شده به روش TLBO بر روی سامانه روتور دوقلو
محورهای موضوعی : مهندسی برق و کامپیوترمصطفی یزدانی 1 , خسرو خانداني 2
1 - دانشکده فنی- مهندسی، دانشگاه اراک
2 - دانشکده فنی- مهندسی، دانشگاه اراک
کلید واژه: الگوریتم آموزش و یادگیری, PID, روتور دوقلو,
چکیده مقاله :
در این مقاله، نحوه طراحی و پیاده‌سازییک کنترل‌کننده PID جهت پایدارسازییک سامانه روتور دوقلوییک درجه آزادی ارائه می‌گردد. با بهره‌گیری از الگوریتم آموزش و یادگیری (TLBO)، ضرایب کنترل‌کننده PID به‌صورت بهینه تنظیم می‌شوند و سپس این کنترل‌کننده بر روی سامانه روتور دوقلویی که در آزمایشگاه کنترل دانشگاه اراک ساخته شده است، پیاده‌سازی می‌گردد. هدف از کنترل سامانه روتور دوقلو، پایدارسازی سامانه در حالت صفر درجه افقی است. مدل‌سازی سامانه غیرخطی روتور دوقلو در فضای حالت انجام می‌شود و از مدل به‌دست‌آمده جهت تنظیم بهینه ضرایب کنترل‌کننده PID با روش آموزش و یادگیری استفاده می‌شود. نتایج به‌دست‌آمده با روش آموزش و یادگیری با چند روش فراابتکاری دیگر شامل الگوریتم ازدحام ذرات، الگوریتم ژنتیک، الگوریتم رقابت استعماری و الگوریتم تکامل تفاضلی مقایسه می‌گردد.با استفاده از همه روش‌ها، سامانه با اندکی خطا به پایداری قابل قبولی رسیده است. با وجود این با بهینه‌سازی توسط الگوریتم آموزش و یادگیری، پایدارسازی و عملکرد سریع‌تر سامانه کنترل در مقایسه با روش‌های فراابتکاری دیگر قابل مشاهده است. مزیت عمده استفاده از روش آموزش و یادگیری، عدم وجود پارامترهای کنترلی است که استفاده از آن را راحت می‌کند. نتایج پیاده‌سازی آزمایشگاهی نیز اثربخشی و کارایی نتایج به‌دست‌آمده از شبیه‌سازی را تأیید می‌کنند.
In this research, a new intelligent control design using Teaching-Learning-Based-Optimization (TLBO) algorithm to optimize PID controller coefficients is presented. This method has been applied on the twin rotor system which has been constructed in Control Engineering Lab at Arak University. The purpose of controlling the twin rotor system is to stabilize the system in the zero degree horizontal position. After modeling and obtaining the state space description, the PID controller is designed and implemented on the system. In this study, by reviewing meta-heuristic optimization methods such as particle swarm optimization algorithm, genetic algorithm, colonial competition algorithm and differential evolution algorithm, the optimization results were compared with the above-mentioned meta-heuristic methods. With the optimization performed by the teaching and learning algorithm, the stability and faster performance of the system compared to other meta-heuristic methods can be seen. The merit of TLBO is that it does not have control parameters, which makes it convenient to employ. The simulation results of the PID controller for a twin rotor system show the effectiveness of the proposed methods.
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