رهگیری اهداف هوایی توسط موشک آشیانه یاب با استفاده از کنترل تطبیقی یادگیری عمیق و فازی

سوری, محمد مهدی; ساداتی, سید حسین

doi:10.22034/jtae.2025.9.1.4

رهگیری اهداف هوایی توسط موشک آشیانه یاب با استفاده از کنترل تطبیقی یادگیری عمیق و فازی

نوع مقاله : علمی پژوهشی

نویسندگان

محمد مهدی سوری ¹

سید حسین ساداتی ²

¹ دانشجوی دکتری, دانشکده مهندسی مکانیک، دانشگاه صنعتی خواجه نصیرالدین طوسی، تهران،ایرن

² دانشیار، دانشکده مهندسی مکانیک، دانشگاه صنعتی خواجه نصیرالدین طوسی، تهران، ایران

10.22034/jtae.2025.9.1.4

چکیده

در رویکرد هدایت وکنترل یکپارچه، قانون هدایت به‌طور جداگانه توسعه یافته و با فرض ایده‌آل بودن خودخلبان آزموده می‌شود. خودخلبان نیز به‌طور مستقل طراحی شده و با فرض ایده‌آل بودن قانون هدایت آزموده می‌شود. این مقاله به تشریح روند طراحی و شبیه‌‌سازی عملکرد کنترل‌‌کننده‌‌ تطبیقی یادگیری عمیق و فازی می‌‌پردازد، که به منظور هدایت موشک در یک سناریوی سه بعدی جهت کمینه‌‌سازی زمان برخورد و فاصله عدم برخورد به هدف ایجاد شده ‌‌است. در طراحی کنترل کننده، ابتدا یک کنترل کننده شبکه عصبی یادگیری عمیق بصورت آفلاین طراحی شده و بصورت جدول بهره در کنترل کننده تطبیقی استفاده می‌شود. در ادامه، با اضافه کردن کنترل فازی، توانمندی این کنترل کننده افزایش می‌یابد. عملکرد هر دو کنترل کننده در حضور اغتشاش مورد ارزیابی قرار میگیرد و با توجه به شبیه‌سازی‌ها نشان داده شد که استفاده از این کنترل کننده‌های پیشنهادی و به‌کارگیری مدل هدایت و کنترل یکپارچه، فاصله عدم برخورد نهایی موشک به هدف و زمان برخورد در مقایسه با کنترل کننده PIDو LQR کاهش یافته است.

کلیدواژه‌ها

موشک

کنترل تطبیقی

شبکه عصبی یادگیری عمیق

کنترل فازی

موضوعات

مکانیک پرواز/ناوبری/کنترل/...

عنوان مقاله English

Air target tracking by homing missile using deep learning and fuzzy adaptive control

نویسندگان English

Mohamad Mahdi Soori ¹

Seyed Hosein Sadati ²

¹ Faculty of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran

² Faculty of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran

چکیده English

In the integrated guidance and control approach, the guidance law and the autopilot are traditionally developed and tested separately, assuming the ideality of each other. This paper presents the design and simulation of a deep and fuzzy learning adaptive controller to guide a homing missile in a three-dimensional scenario to minimize collision time and maximize target interception accuracy. A deep learning neural network controller is initially developed offline in the proposed controller design and utilized as a gain table within the adaptive control framework. Subsequently, introducing fuzzy control further enhances the controller's adaptability and performance. The effectiveness of both controllers is evaluated under disturbance conditions. Simulation results demonstrate that the proposed controllers, along with the integrated guidance and control model, achieve reduced final miss distance and collision time compared to conventional PID and LQR controllers.

کلیدواژه‌ها English

Missile

Adaptive control

Deep learning neural network

Fuzzy control

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