کنترل تطبیقی بدون مدل بهبود یافته با استفاده از شبکه عصبی در حضور داده‌های کوانتیزه شده

نوع مقاله : علمی-پژوهشی

نویسندگان

1 گروه مهندسی برق، دانشکده فنی و مهندسی، دانشگاه شهید باهنر کرمان، کرمان، ایران

2 عضو هیات علمی دانشگاه شهید باهنر کرمان

3 electrical department of Kerman university

چکیده

این مقاله یک ساختار بهبود ‌یافته برای کنترل تطبیقی بدون مدل مبتنی بر پدیده کوانتیزاسیون داده‌ها را بر پایه شبکه عصبی GMDH (روش گروهی پردازش داده‌ها) بنا شده است. در این مطالعه، به منظور غلبه بر اثرات مخرب کوانتیزاسیون بر روی داده‌های سیستم، داده‌های کوانتیزه شده سیستم مورد مطالعه ابتدا به بلوک شبکه عصبی منتقل شده، سپس شبکه عصبی با تکیه بر قابلیت پیش‌بینی، داده‌های خروجی واقعی سیستم مورد مطالعه را تخمین می­زند. در ادامه داده‌های تخمین زده شده توسط شبکه عصبی، جهت تولید سیگنال کنترلی به بلوک کنترل کننده تطبیقی بدون مدل منتقل می­شوند. به این ترتیب کنترل کننده تطبیقی بدون مدل با در دست داشتن داده‌های واقعی خروجی و تنها با محاسبه یک رابطه بازگشتی ساده، سیگنال کنترلی مطلوب را تولید خواهد کرد. سپس سیگنال کنترلی تولید شده توسط این کنترل کننده، به منظور برآورده سازی هدف کنترلی، به سیستم مورد مطالعه اعمال خواهد شد. پایداری ساختار کنترلی پیشنهاد شده در این پایان نامه توسط نظریه لیاپانوف بررسی شده است. نتایج شبیه‌سازی نیز برتری ساختار کنترلی پیشنهاد شده در این پایان نامه را نسبت به کنترل کننده تطبیقی بدون مدل مرسوم در حضور داده‌های کوانتیزه شده نشان می­دهد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

GMDH Neural Network-Based Enhanced Data-Driven Adaptive Control Design for Unknown Nonlinear Systems in the Presence of Quantized Data.

نویسندگان [English]

  • Mohammadreza Mir 1
  • Malihe Maghfoori Farsangi 2
  • یاسین Asadi 3
  • Mohammad Mollaie Emamzadeh 1
1 Department of Electrical Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
2 Department of Electrical Engineering, Shahid Bahonar University of Kerman
3 دانشکده فنی و مهندسی شهید باهنر کرمان، گروه برق
چکیده [English]

This paper proposes an Enhanced Data-Driven Quantized Model-Free Adaptive Control (EDD-QMFAC) structure for a class of unknown nonlinear systems based on the Group Method of Data Handling (GMDH) neural network. In this study, the output quantized data is given to the GMDH block to overcome the data quantization challenges in Data-Driven Control methods. In the proposed control loop the GMDH derives a model to estimate the actual output of the system from the quantized output data based on the predictive feature of this network. The controller then generates the input control signal based on the system’s estimated actual output data. The Lyapunov theory is used to prove the stability of the suggested structure. The simulation results demonstrate the advantages of the proposed control structure over the conventional QMFAC.

کلیدواژه‌ها [English]

  • Data-Driven Control
  • MFAC method
  • GMDH Neural Network
  • Data Quantization

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