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.


1.      Wu, J., Wang, J. and You, Z., "An overview of dynamic parameter identification of robots", Robotics and Computer-Integrated Manufacturing,  Vol. 26, No. 5, (2010), 414-419.

2.      Gu, Y. and Ding, R., "A least squares identification algorithm for a state space model with multi-state delays", Applied Mathematics Letters,  Vol. 26, No. 7, (2013), 748-753.

3.      Brunot, M., Janot, A., Carrillo, F., Garnier, H., Vandanjon, P.-O. and Gautier, M., "Physical parameter identification of a one-degree-of-freedom electromechanical system operating in closed loop", IFAC-PapersOnLine,  Vol. 48, No. 28, (2015), 823-828.

4.      Jubien, A., Gautier, M. and Janot, A., "Dynamic identification of the kuka lwr robot using motor torques and joint torque sensors data", IFAC Proceedings Volumes,  Vol. 47, No. 3, (2014), 8391-8396.

5.      Bayani, H., Masouleh, M.T. and Kalhor, A., "An experimental study on the vision-based control and identification of planar cable-driven parallel robots", Robotics and Autonomous Systems,  Vol. 75, (2016), 187-202.

6.      Wang, W., Ding, F. and Dai, J., "Maximum likelihood least squares identification for systems with autoregressive moving average noise", Applied Mathematical Modelling,  Vol. 36, No. 5, (2012), 1842-1853.

7.      El-Kafafy, M., Peeters, B., Guillaume, P. and De Troyer, T., "Constrained maximum likelihood modal parameter identification applied to structural dynamics", Mechanical Systems and Signal Processing,  Vol. 72, (2016), 567-589.

8.      Chang, W.-D., "Nonlinear system identification and control using a real-coded genetic algorithm", Applied Mathematical Modelling,  Vol. 31, No. 3, (2007), 541-550.

9.      Yang, Z. and Seested, G.T., "Time-delay system identification using genetic algorithm–part one: Precise fopdt model estimation", IFAC Proceedings Volumes,  Vol. 46, No. 20, (2013), 561-567.

10.    West, C., Montazeri, A., Monk, S.D. and Taylor, C.J., "A genetic algorithm approach for parameter optimization of a 7dof robotic manipulator", IFAC-PapersOnLine,  Vol. 49, No. 12, (2016), 1261-1266.

11.    Sharifzadeh, M., Arian, A., Salimi, A., Masouleh, M.T. and Kalhor, A., "An experimental study on the direct & indirect dynamic identification of an over-constrained 3-dof decoupled parallel mechanism", Mechanism and Machine Theory,  Vol. 116, (2017), 178-202.

12.    Tavakolpour, A.R., Darus, I.Z.M., Tokhi, O. and Mailah, M., "Genetic algorithm-based identification of transfer function parameters for a rectangular flexible plate system", Engineering Applications of Artificial Intelligence,  Vol. 23, No. 8, (2010), 1388-1397.

13.    Zare, S. and Tavakolpour-Saleh, A., "Frequency-based design of a free piston stirling engine using genetic algorithm", Energy,  Vol. 109, (2016), 466-480.

14.    Li, C. and Zhou, J., "Parameters identification of hydraulic turbine governing system using improved gravitational search algorithm", Energy Conversion and Management,  Vol. 52, No. 1, (2011), 374-381.

15.    Sedighizadeh, M. and Kashani, M.F., "A tribe particle swarm optimization for parameter identification of proton exchange membrane fuel cell", International Journal of Engineering-Transactions A: Basics,  Vol. 28, No. 1, (2014), 16-25.

16.    Sitarz, P. and Powałka, B., "Modal parameters estimation using ant colony optimisation algorithm", Mechanical Systems and Signal Processing,  Vol. 76, (2016), 531-554.

17.    Ding, L., Wu, H., Yao, Y. and Yang, Y., "Dynamic model identification for 6-dof industrial robots", Journal of Robotics,  Vol. 2015, (2015), 9-15.

18.    Tavakolpour-Saleh, A., Zare, S. and Badjian, H., "Multi-objective optimization of stirling heat engine using gray wolf optimization algorithm", International Journal of Engineering-Transactions C: Aspects,  Vol. 30, No. 6, (2017), 321-329.

19.    Brancati, R., Russo, R. and Savino, S., "Method and equipment for inertia parameter identification", Mechanical Systems and Signal Processing,  Vol. 24, No. 1, (2010), 29-40.

20.    Lopéz, R., Gonzalez, I., Flores, J., Ordaz, J., Salazar, S. and Lozano, R., "Real time parameter identification of the inertia tensor for a quad-rotor mini-aircraft using adaptive control", IFAC Proceedings Volumes,  Vol. 46, No. 30, (2013), 32-37.

21.    Naghipour, M., "Large scale experiments data analysis for estimation of hydrodynamic force coefficients", International Journal of Engineering-Transactions A: Basics,  Vol. 16, No. 1, (2002), 29-35.

22.    Zhao, L., Zhou, C. and Yu, Y., "Damping parameters identification of cabin suspension system for heavy duty truck based on curve fitting", Shock and Vibration,  Vol. 2016, (2016).

23.    Xu, L., "The damping iterative parameter identification method for dynamical systems based on the sine signal measurement", Signal Processing,  Vol. 120, (2016), 660-667.

24.             Preumont, A., "Mechatronics: Dynamics of electromechanical and piezoelectric systems, Springer Science & Business Media,  Vol. 136,  (2006).

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