بهبود کنترل بازوی رباتیک به کمک کنترل¬کننده تطبیقی مدل مرجع با استفاده از طبقه‌بندی سیگنال¬های EMG
محورهای موضوعی : مهندسی برق و کامپیوتر
مهسا برفی
1
,
حمیدرضا کرمی
2
,
الهام فراهی
3
,
فاطمه فریدی
4
,
سید منوچهر حسینی پیلانگرگی
5
1 - دانشگاه بوعلی سینا،دانشکده فنی و مهندسی
2 - دانشگاه بوعلی سینا،دانشکده فنی و مهندسی
3 - دانشگاه بوعلی سینا،دانشکده فنی و مهندسی
4 - دانشگاه بوعلی سینا
5 - دانشگاه بوعلی سینا،دانشکده فنی و مهندسی
کلید واژه: الکترومیوگرافی, آنالیز تشخیصی خطی, ربات دو درجه آزادی, کنترل‌کننده تطبیقی مدل مرجع,
چکیده مقاله :
هدف این مقاله، بهبود کنترل بازوی رباتیک به کمک کنترل‌کننده تطبیقی مدل مرجع مبتنی بر نظریه لیاپانوف با استفاده از طبقه‌بندی سیگنال‌های الکترومیوگرام (EMG) است. در این مقاله، بازوی انسان با یک ربات دو درجه آزادی مدل‌سازی شده است. روش کنترلی پیشنهادی، کنترل‌کننده تطبیقی مدل مرجع است. ماحصل این پژوهش، طراحی و شبیه‌سازی بازوی رباتیک به همراه کنترل‌کننده تطبیقی مدل مرجع است که با استفاده از طبقه‌بندی داده‌های EMG ثبت‌شده از حرکات بازوی انسان، منتج به ردیابی مناسب سیگنال مرجع، فراجهش و خطای حالت ماندگار کمتر در مقایسه با کنترل‌کننده مرسوم PI شده است. بدین منظور ابتدا داده‌های EMG با استفاده از دو الکترود از عضلات دلتوئید قدامی و دلتوئید میانی بازوی پنج دختر و با انجام دو حرکت دورشدن (ابداکشن) و خم‌شدن (فلکشن) بازو جمع‌آوری شده و پس از رفع نویز، ویژگی‌های ریاضی انتگرال مقدار مطلق، گذر از صفر، واریانس و فرکانس میانه از آنها استخراج می‌شود. سپس کلاس‌بندی به روش آنالیز تشخیصی خطی به منظور تشخیص حرکات بر اساس ویژگی‌های داده‌ها صورت می‌گیرد. در نهایت، مدل و سیستم کنترل‌کننده پیشنهادی با توجه به ویژگی‌های سیگنال EMG، برای دستیابی به پاسخ کنترلی مناسب، طراحی می‌شوند و سیگنال فرمان مناسب جهت انجام حرکت مربوطه به کنترل‌کننده ارسال می‌شود. نتایج و مقادیر خطاهای حاصل‌شده نشان می‌دهند که انطباق رفتار مدل و کنترل‌کننده حاصل با الگوی از پیش تعریف شده حرکتی قابل توجه و مورد تأیید است.
The purpose of designing and manufacturing prosthetic organs is to create their maximum behavioral similarity to human organs. The aim of this paper is to improve the robotic arm control via Model Reference Adaptive System (MRAS) based on Lyapunov theory using EMG data classification. In this paper, human arm is modeled with a robot with two degrees of freedom. The proposed control method is MRAS. The outcome of this research is a robotic arm with MRAS, using the classification of electromyogram (EMG) data recorded from human arm movements, results in proper tracking of the reference signal, less overshoot and steady-state error compared to the conventional PI controller. For this purpose, using two electrodes, EMG data is collected from the anterior deltoid and middle deltoid muscles of the arm of five female athletes and by performing two movements of abduction and flexion of the arm. Then, after eliminating noise, integral of absolute value (IAV), zero crossing (ZC), variance (VAR) and median frequency (MF) are extracted. Then, classification is done by linear discriminant analysis (LDA) method to detect movements based on data characteristics. Finally, the proposed controller and model are designed according to the EMG characteristics to achieve the proper control response and the appropriate command signal is sent to the controller to perform the corresponding movement. The results and the values of the obtained errors show the conformity of the model and controller behavior with the predefined movement pattern.
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