IJE TRANSACTIONS C: Aspects Vol. 31, No. 3 (March 2018) 480-486    Article in Press

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M. H. Sangdani and A. Tavakolpour-Saleh
( Received: September 11, 2017 – Accepted in Revised Form: October 12, 2017 )

Abstract    In this paper, the uncertain dynamic parameters of an experimental target tracker robot are identified through the application of genetic algorithm. The considered serial robot is a two-degree-of-freedom dynamic system with two revolute joints in which damping coefficients and inertia terms are uncertain. First, dynamic equations governing the robot system are extracted and then, simulated numerically. Next, an open-loop experiment with finite duration step inputs is implemented on the experimental setup to collect practical output data. Accordingly, a desired objective function is defined as the sum of discrepancy between the experimental and simulated output data. Subsequently, a genetic algorithm is employed to explore the best damping coefficients and inertia terms of the simulation scheme so as to minimize the presented cost function and taking into account the same input data for both simulation and experiment. Finally, the simulated output data based on the identified robot parameters reveal an acceptable agreement with the measured outputs through which validity of the identification scheme is affirmed.


Keywords    Parameter identification, target tracker robot, genetic algorithm


چکیده    در این مقاله از الگوریتم ژنتیک برای تخمین پارامترهای دینامیکی یک ربات ردیاب با نتایج شبیه‌سازی و آزمایشگاهی استفاده‌شده است. ربات ردیاب دو درجه آزادی دارد که از دو حرکت چرخشی تشکیل‌شده است. به‌صورت کلی، روند متعارف شناسایی ربات شامل مدل‌سازی، طراحی آزمایشگاهی، داده‌برداری، پردازش سیگنال، تخمین داده و اعتبار سنجی هست. بر اساس این روند، ابتدا معادلات دینامیکی ربات محاسبه شده و در برنامه سیمولینک متلب شبیه‌سازی شده است. برای مرحله بعد، ربات طراحی و ساخته شده است. سپس برای جمع­آوری داده، آزمایش حلقه باز صورت گرفته است. در این پژوهش برای شناسایی پارامترها، الگوریتم ژنتیک به کار گرفته شده است. برای این کار، یک ورودی یکسان به مدل ربات و خود ربات اعمال می­شود. اختلاف بین خروجی­ها به‌عنوان تابع هزینه الگوریتم ژنتیک در نظر گرفته می­شود. درنهایت، الگوریتم ژنتیک به کار گرفته‌شده مقادیر مناسب پارامترها را تخمین می­زند. نهایتاً نتایج، تطبیق خیلی خوبی را بین نتایج آزمایشگاهی و نتایج شبیه­سازی با پارامترهای بدست آمده، نشان می­دهد.


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