طراحی بهینه پارتویی کنترلرهای خطی به کمک الگوریتم چند هدفه اجتماع-ذرات

پذیرفته شده برای ارائه شفاهی ، صفحه 1-8 (8)
کد مقاله : 1033-ISAV2022 (R1)
نویسندگان
1رفسنجان- دانشگاه ولی عصر رفسنجان- دانشکده مهندسی مکانیک-
2کرمان، سیرجان، دانشگاه صنعتی سیرجان، دانشکده مهندسی مکانیک
چکیده
بهینه‌سازی هنر یافتن بهترین پاسخ در میان شرایط موجود است و برای طراحی و نگهداری بسیاری از سیستم‌های مهندسی، اقتصادی، صنایع و حتی اجتماعی به منظور به حداقل رساندن هزینه‌ها یا به حداکثر رساندن سود استفاده می‌شود. به منظور حل چنین مسائلی از الگوریتم‌های بهینه‌سازی استفاده می‌شود. در دهه‌های اخیر، الگوریتم‌های الهام گرفته از طبیعت به عنوان روش-های بهینه‌سازی هوشمند در کنار روش‌های کلاسیک موفقیت قابل توجهی از خود نشان داده‌اند. در این مطالعه پژوهشی، یک روش بهینه‌سازی چند‌هدفه جدید برای یافتن جبهه‌های پارتویی توابع هدف غیربرتر در طراحی کنترل‌کننده‌های پسخورد حالت خطی برای یک سیستم گوی و میله استفاده شده است. در فرایند حل مسئله، بهره‌های کنترلی به عنوان متغیر طراحی و مجموع نرمال شده زمان‌نشست و فراجهش به عنوان تابع هدف لحاظ شده‌اند. نتایج و تحلیل‌های به دست آمده نشان می‌دهد که الگوریتم بهینه‌سازی چند‌هدفه ارائه شده در این مقاله از نظر سرعت همگرایی، بهینه سراسری، دقت حل و قابلیت اطمینان بسیار خوب عمل می‌کند.
کلیدواژه ها
موضوعات
 
Title
Pareto Design of Linear Controllers Based on a Multi-objective Algorithm;
Authors
ali reza shafiee sarvestani, mohammadjavad Mahmoodabadi
Abstract
Optimization is the art of finding the best answer among existing conditions and is used to design and maintain many engineering, economic, industrial and even social systems in order to minimize costs or maximize profits. In order to solve such problems, optimization algorithms are used. In recent decades, algorithms inspired by nature have shown significant success as smart optimization methods alongside classical methods. In this research study, a new multi-objective optimization method is applied to obtain the Pareto frontiers of some non-commensurable objective functions in the design of linear state feedback controllers for a ball-beam system. In the process of solving the problem, the control gains are included as the design variable and the normalized sum of the sitting time and overshoot as the objective function. The obtained results and analysis show that the multi-objective optimization algorithm presented in this article works very well in terms of convergence speed, global optimality, solution accuracy and reliability.
Keywords
Particle swarm optimization, Multi-objective Algorithm, Linear State Feedback Control, ball-beam system
مراجع
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