مهندسی مکانیک مدرس

مهندسی مکانیک مدرس

طراحی کنترل‌کننده‌ی ترکیبی تطبیقی هوشمند با استفاده از شبکه‌های عصبی موجکی کوانتومی برای ردیابی مسیر سیستم‌های کوانتومی بسته‌ی متناهی

نویسندگان
1 گروه ریاضی و علوم کامپیوتر، دانشکده علوم پایه، دانشگاه لرستان، خرم‌آباد، ایران
2 عضو هیات علمی دانشگاه لرستان
چکیده
در این مقاله، یک کنترل‌کننده‌ی ترکیبی تطبیقی هوشمند جدید برای ردیابی یک مسیر دینامیک در سیستم‌های کوانتومی بسته‌ی متناهی ارائه شده است. مشکل بروز تکینی‌های ذاتی در سیگنال‌های کنترل‌کننده‌ی ردیابی مسیرهای دینامیکی در کنترل سیستم‌های کوانتومی، منجر به رشد شدید دامنه‌ی سیگنال‌های کنترل و در نتیجه افزایش هزینه‌ی کنترل و ناپایداری سیستم کنترل می‌شود. ابتدا بر اساس تئوری پایداری لیاپانوف یک کنترل‌کننده‌ی تطبیقی برای ردیابی مسیر دینامیک طراحی می‌شود. سپس برای رفع مشکل تکینی در سیگنال‌های کنترل تطبیقی، یک کنترل‌کننده‌ی هوشمند کوانتومی مبتنی بر شبکه‌ی عصبی موجکی تطبیقی کوانتومی با قوانین یادگیری پس‌انتشار دسته‌ای طراحی و بوسیله‌ی یک پارامتر ناظر تکینی با کنترل‌کننده‌ی تطبیقی ترکیب شده است. کنترل‌کننده‌ی ارائه شده با ترکیب موثر سیگنال‌های کنترل تطبیقی و هوشمند، حالت سیستم کوانتومی را طوری تنظیم می‌کند که فرآیند ردیابی مسیر دینامیک از پیش تعیین شده، به خوبی کنترل می‌شود. کنترل‌کننده‌ی پیشنهادی علاوه بر ردیابی مسیر هدف، اثرات نامطلوب ناشی از بروز پدیده‌ی تکینی و دامنه بزرگ سیگنال‌های کنترل را حذف می‌کند. عملکرد کنترل‌کننده‌ی ترکیبی تطبیقی هوشمند پیشنهادی در مساله‌ی کنترل انتقال جمعیت یک سیستم کوانتومی بسته‌ی چهار سطحی در ردیابی مسیر دینامیک پاسخ پله، مورد بررسی قرار گرفته است. بررسی نتایج شبیه‌سازی کاهش خطای ردیابی، کم شدن هزینه‌ی کنترل با تنظیم موثر سیگنال‌های کنترل و کاهش قابل ملاحظه‌ی تعداد دفعات بروز پدیده‌ی تکینی را نشان می‌دهد.
کلیدواژه‌ها

عنوان مقاله English

Hybrid adaptive intelligent controller design using quantum wavelet neural networks for trajectory tracking control in finite dimensional closed quantum systems

نویسندگان English

Zeinab Sahebi 1
Majid Yarahmadi 2
1 Department of Mathematics and Computer Science, Faculty of Science, Lorestan University, Khorramabad, Iran
چکیده English

In this paper, a new hybrid adaptive intelligent controller is introduced to track a dynamic trajectory in finite dimensional closed quantum systems. The problem of inherent singularities in control signals of trajectory tracking in quantum systems leads to a sharp increase in control signal amplitude. As a result, the amplitude of the large signal increases the control cost and control system instability. Consequently, the large control signal amplitude increases the control cost and leads to instability in control system. Firstly, according to the Lyapunov stability theory, an adaptive controller is designed to track the dynamic path. Then, to overcome the singularity drawback, a quantum intelligent controller is designed based on a quantum adaptive wavelet neural network with batch back propagation learning and combined with adaptive controller by a singularity observer. The proposed hybrid adaptive intelligent controller by combining the adaptive and intelligent control signals adjusts the quantum state so that the desired dynamic trajectory is traced effectively and simultaneously eliminates the effects of singularities and reduces the control amplitude. The performance of the hybrid adaptive intelligent controller is checked for step response tracking in a population transfer of a four-level closed quantum system. The simulation results show that the introduced controller reduces the tracking error and significantly decreases the number of singular points. Also, the control cost is reduced by effective adjustment of the control signal’s amplitude.

کلیدواژه‌ها English

Quantum Adaptive Control
Quantum Wavelet Neural Network
Quantum Intelligent Control
Quantum Trajectory Tracking
Quantum Hybrid Controller
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