مدل‌سازی بازوی مکانیکی دانش‌بنیان بالابر باسکول‌دار با رویکرد شبیه‌سازی و استنتاج فازی

نوع مقاله : مقاله پژوهشی (کاربردی)

نویسندگان

1 دانشجوی دکتری، گروه مدیریت صنعتی، واحد فیروزکوه، دانشگاه آزاد اسلامی، فیروزکوه، ایران.

2 استادیار، گروه مدیریت صنعتی، واحد فیروزکوه، دانشگاه آزاد اسلامی، فیروزکوه، ایران.

چکیده

تاکنون کنترل‌کننده‌ها اغلب از معادلات حاکم بر سینماتیک مستقیم یا سینماتیک معکوس، با هدف کنترل موقعیت مجری نهایی بازوی رباتیک استفاده می‌کردند. حل دشوار معادلات سینماتیک مستقیم و سینماتیک معکوس، خطا در حل معادلات، نبود محیط گرافیکی کاربرپسند، انعطاف‌ناپذیری در تصمیم‌گیری کنترل‌کننده و حجم زیاد محاسبات از مشکلات سیستم‌های کنترلی موجود در کنترل بازوهای رباتیک می‌باشد. در این مقاله، ربات بالابر باسکولدار توسط دو روش کنترلی PID کلاسیک و Fuzzy و با تعداد درجه آزادی 4 به همراه ارائه شبیه‌سازی که در آن چهار قسمت از بازو توسط کنترل‌کننده بررسی شده و از Matlab/Simulink به‌عنوان ابزار برای آزمایش ویژگی‌های حرکتی بازوی ربات استفاده شده است. خروجی‌های پیاده­سازی نشان‌دهنده عملکرد رضایت‌بخش کنترل‌کننده فازی طراحی شده است که توانسته با درصد بالازدگی و زمان نشست مطلوب 1.23 ثانیه به کنترل بازوهای این ربات بپردازد. همچنین به‌منظور آزمون عملکرد ربات بالابر باسکولدار، گره‌های حرکت بازو را هم به‌صورت مطلوب قرار داده و سیستم موردکنترل نشان از دقت بالای کنترل‌کننده فازی دارد؛ به‌طوری که بعد از گذشت 1.23 ثانیه در نقطه بحرانی یعنی مچ دست ناحیه wrisrt توانسته با نشستی مطلوب با خطای ماندگار صفر و بدون هیچ به‌هم‌خوردگی تداوم پیدا کند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Knowledge-based Mechanical Arm Modeling of Bascule Lift with Simulation Method and Fuzzy Inference Approach

نویسندگان [English]

  • Reza Rahbar Hadi Bigloo 1
  • Mohammad Mehdi Movahedi 2
1 PhD Student, Department of Industrial Management, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran.
2 Assistant Professor, Department of Industrial Management, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran.
چکیده [English]

Thus far, controllers have often used the equations governing direct kinematics or reverse kinematics to control the position of the final implement of the robotic arm. Difficulties in unravelling direct kinematic equations and reverse kinematics, errors in solving equations, lack of user-friendly graphical environment, lack of flexibility in controller decision making and large volumes of calculations are problems of control systems in controlling robotic arms. In this paper, a skeletal lift robot with two classic PID and Fuzzy control methods with 4 degrees of freedom, in addition to a simulation in which four parts of the arm were examined by the controller and Matlab / Simulink as a tool for feature testing were used. Robotic arm movements were used. Implementation outputs show the satisfactory performance of the fuzzy controller which was able to control the arms of this robot with a percentage of uplift and a desired sitting time of 1.23 seconds. Furthermore, in order to test the performance of the scaffolding robot, the movement nodes of the arm were placed optimally and the controlled system showed the high accuracy of the fuzzy controller so that after 1.23 seconds at the critical point, the wrist area, it was able to continue with the desired sitting with a permanent error of zero and without any distortion.

کلیدواژه‌ها [English]

  • Palletizer
  • Robot arm
  • 4DOF
  • Controller PID
  • Controller fuzzy
[1] Taghi Rad, H. (2017). Introduction to automation and industrial control processes (3 ed.). Khajeh Nasir al-Din Tusi University of Technology. https://press.kntu.ac.ir/b ook_388123.html
[2] Nemati, A., & Bakhshizadeh, Y. (2017, May 5). Improving robot arm performance using fuzzy logic controller and comparing it with PID controller. Third National Conference on New Approaches in Computer and Electrical Engineering, Rudsar, Iran. https:// civilica.com/doc/657955/
[3] Azgoli, S., Moradian Lotfi A., & Taghi Rad, H. (2006, May 15). Design and implementation of controller for detailed elastic robot with fuzzy observer ring to solve problems caused by operator saturation. 14th Annual Conference of Mechanical Engineering, Esfahan, Iran. https://civilica.com/doc/27789/
[4] Ogata, K. (2012). Digital control systems (two volumes) (7 ed.). University of Tehran ht tps://press.ut.ac.ir/book_268.html
[5] Soltanpour, M. R. (2012). Variable Structure Tracking Control of Robot Manipulator in Task Space in the Presences of Structure and Unstructured Uncertainties in Dynamics and Kinematics. Journal of Solid and Fluid Mechanics, 1(1), 81-88. https://doi.org/10.22044/jsfm.2012.29
[6] Ebrahimi, Z., & Chatraei, A.,& Shah Nazari, O.,& Pour Rahim, M. (2017, September 14). Design and practical implementation of intelligent controller for border guard robot. 2nd International Conference on Electrical Engineering, Qarchak, Iran. https://civilica. com/doc/698674/
[7] Abedi, A., & Sakhavati, A. (2014, April 22). PID-ICA controller design for robot arm drive motor. The 22nd Annual International Conference on Mechanical Engineering, Ahvaz, Iran. https://civilica.com/doc/277346/
[8] Khoury, G. M., Saad, M., Kanaan, H. Y., & Asmar, C. (2004). Fuzzy PID Control of a Five DOF Robot Arm. Journal of Intelligent and Robotic Systems, 40(3), 299-320. https://doi.org/10.1023/B:JINT.0000038947.97195.22
[9] Alassar, A. Z., Abuhadrous, I. M., & Elaydi, H. A. (2010, February 26-28). Modeling and control of 5 DOF robot arm using supervisory control. 2010 The 2nd International Conference on Computer and Automation Engineering, Singapore https://doi.org/10.1109/ICC AE.2010.5451398
[10] Talebi, N. (2017, July 5). Design of a robust controller for tracking the position in robots. National Conference on New Research in Electrical, Computer and Medical Engineering, Kazerun, Iran. https://civilica.com/doc/658153/
[11] Rahimi, A., & Rezaei, S., & Parvizian, J. (2017, May 24). Scara robot design 4 degrees of freedom of flexible assembly unit. The first national congress on the use of materials and advanced manufacturing in industries Tehran, Iran. https://civilica.com/doc/673941/
[12] Davari, M., & Soheili Najafabadi, F. (2016, May 4). Using fuzzy logic to mathematically infer angles and effective point positions in robotic arm joints. The Second National Conference on New Approaches in Computer and Electrical Engineering, Rudsar, Iran. https://civilic a.com/doc/522666/
[13] Basu, R., & Padage, S. (2017). Development of 5 DOF Robot Arm -Gripper for sorting and investigating RTM Concepts. Materials Today: Proceedings, 4(2), 1634-1643. https:// doi.org/10.1016/j.matpr.2017.02.002
[14] Sabri, M. (2017). Stabilization and control of the power system using meta-heuristic algorithms. Karafan Quarterly Scientific Journal, 14(2), 33-55. https://karafan.tvu.ac.ir/article _100504_13ffdcdd667a867abcbf8d00c439c081.pdf
[15] Masoumnezhad, M., Kazemi, M., Askari, N., Taheri, M. H., & Ghamati, M. (2021). Semi-Analytical Solution of Unsteady Newtonian Fluid Flow and Heat Transfer between two Oscillation Plate under the Influence of a Magnetic Field. Karafan Quarterly Scientific Journal, 18(1), 35-62. https://doi.org/10.48301/kssa.2021.131037
[16] Olmedo, N. A., Barczyk, M., Zhang, H., Wilson, W., & Lipsett, M. G. (2020). A UGV-based modular robotic manipulator for soil sampling and terramechanics investigations. Journal of Unmanned Vehicle Systems, 8(4), 364-381. https://doi.org/10.1139/juvs-2020-0003
[17] Jafarian, S., & Shamisa, A. (2019, November 1). Design and simulation of fuzzy slip resistant resistor controller for movable mechanical arm claw. Third National Conference on Electrical and Computer Engineering, Tehran, Iran. https://civilica.com/doc/1005855/
[18] Nejad Korki, N., & Mahmoudabadi, M. J. (2019, November 20). Design of a fuzzy adaptive controller for a mechanical arm. 3rd International Conference on Soft Computing, Rudsar, Iran. https://civilica.com/doc/1005950/
[19] Masihabadi, S., & Akbarzadeh Kalat, A. (2019, May 29). Implement trans-local model control on a skilled mechanical arm. Fourth National Conference on Electrical and Computer Engineering, Tehran, Iran. https://civilica.com/doc/989090/
[20] Soto-Hidalgo, J. M., Vitiello, A., Alonso, J. M., Acampora, G., & Alcala-Fdez, J. (2019). Design of Fuzzy Controllers for Embedded Systems With JFML. International Journal of Computational Intelligence Systems, 12(1), 204-214. https://doi.org/10.2991/ijcis.2019 .125905646