دانش و فناوری هوافضا

دانش و فناوری هوافضا

استفاده از شبکه عصبی بهبودیافته به منظور هدایت و کنترل یک موشک رهگیر به سمت اهداف مانوردار

نوع مقاله : مقاله پژوهشی

نویسندگان
1 دانشجوی دکتری، دانشکده مهندسی مکانیک، دانشگاه صنعتی خواجه نصیرالدین طوسی، تهران
2 استادیار، دانشکده مهندسی و پرواز، دانشگاه افسری امام علی(ع)، تهران
چکیده
در این مقاله، طراحی و شبیه‌سازی کنترل‌کننده شبکه عصبی بهینه برای یک موشک زمین به هوا با هدف کاهش زمان پرواز تا برخورد با هدف ارائه شده است. مدل‌سازی موشک و هدف به‌صورت سه‌بعدی انجام شده و بهینه‌سازی برای کمینه‌سازی فاصله و زمان پرواز صورت گرفته است. نوآوری پژوهش در به‌کارگیری الگوریتم‌های ژنتیک و ازدحام ذرات در طراحی کنترل‌کننده شبکه عصبی در چارچوب هدایت و کنترل یکپارچه است. در الگوریتم ژنتیک، با انتخاب جمعیت اولیه و اعمال تابع هدف، بهترین جفت‌ها برای تولید نسل انتخاب می‌شوند. در الگوریتم ازدحام ذرات نیز با به‌روزرسانی موقعیت و اعمال جریمه به ذرات خارج از مرز، فرآیند بهینه‌سازی انجام می‌شود. ابتدا کنترل‌کننده PID طراحی شده و سپس نسخه بهینه مبتنی بر شبکه عصبی ارائه می‌گردد. نتایج شبیه‌سازی نشان می‌دهند که زمان برخورد با هدف با کنترل‌کننده پیشنهادی حدود ۳۸ تا ۴۲ ثانیه نسبت به کنترل‌کننده PID کاهش می‌یابد.
کلیدواژه‌ها

موضوعات


عنوان مقاله English

Using an improved neural network to guide and control an interceptor missile towards maneuvering targets

نویسندگان English

Mohammadmahdi Soori 1
kazem imani 2
1 PhD student, Faculty of Mechanical Engineering, Khajeh Nasir al-Din Toosi University of Technology, Iran.
2 Assistant Professor, Faculty of Engineering and Flight, Imam Ali (AS) Military University, Tehran
چکیده English

In This paper presents the design and simulation of an optimized neural network controller for a surface-to-air missile aimed at minimizing the flight time to the target. The missile and target are modeled in three dimensions, and optimization is performed to reduce both interception distance and flight time. The novelty lies in integrating genetic and particle swarm optimization algorithms to design a neural network controller within a unified guidance and control framework. In the genetic algorithm, an initial population is selected and evaluated using a fitness function, while the best pairs are chosen for reproduction. In the particle swarm optimization, particles update their positions based on previous states, with penalties applied to those exceeding the problem’s bounds. A PID controller is first designed, followed by the proposed optimized neural controller. Simulation results show that the proposed controller, combined with integrated guidance and control, reduces the interception time by approximately 38 to 42 seconds compared to the PID controller.

کلیدواژه‌ها English

Missile
guidance and control
neural control
genetic algorithm
particle swarm algorithm
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