Function Approximation Approach for Robust Adaptive Control of Flexible joint Robots

Authors

1 Assistant Professor, Department of Electrical Engineering, Garmsar Branch, Islamic Azad University, Garmsar, Iran

2 Associate Professor, Department of Mathematics, Garmsar Branch, Islamic Azad University, Garmsar, Iran

Abstract

This paper is concerned with the problem of designing a robust adaptive controller for flexible joint robots (FJR). Under the assumption of weak joint elasticity, FJR is firstly modeled and converted into singular perturbation form. The control law consists of a FAT-based adaptive control strategy and a simple correction term. The first term of the controller is used to stability of the slow dynamics, and the second term is used to damp out the elastic oscillations of the joints. The stability analysis is provided according to the Lyapunov direct method. Simulation results on a single-link FJR demonstrate suitable performance of the proposed control schemes.

Keywords


Article Title [Persian]

روش تقریب توابع برای کنترل تطبیقی مقاوم ربات‌های با مفاصل انعطاف‌پذیر

Authors [Persian]

  • علیرضا ایزد بخش 1
  • اباصلت بداغی 2
1 استادیار گروه مهندسی برق، واحد گرمسار، دانشگاه آزاد اسلامی، گرمسار، ایران
2 دانشیار گروه ریاضی، واحد گرمسار، دانشگاه آزاد اسلامی، گرمسار، ایران
Abstract [Persian]

این مقاله مرتبط با مساله طراحی کنترل کننده تطبیقی مقاوم برای بازوهای ماهر ربات با مفاصل انعطاف پذیر می­باشد. با فرض انعطاف ناچیز در مفاصل ربات، ابتدا معادلات دینامیکی ربات با مفاصل انعطاف پذیر در فرم انحراف تکین بدست می­آید. قانون کنترل متشکل از یک استراتژی کنترل تطبیقی مبتنی بر تکنیک تقریب توابع و یک بخش کنترلی تصحیح کننده می­باشد. بخش اول کنترل کننده به منظور پایدار سازی دینامیک­های کند و بخش دوم به منظور حذف نوسانات در مفاصل بکارگیری می­گردد. آنالیز پایداری از طریق روش مستقیم لیاپانوف صورت می­پذیرد. نتایج شبیه سازی بر روی ربات یک لینکی با مفاصل انعطاف پذیر، حاکی از عملکرد مناسب طرح پیشنهادی می­باشد.

Keywords [Persian]

  • کنترل ترکیبی
  • تکنیک تقریب توابع
  • کنترل تطبیقی مقاوم
[1] L. M. Sweet, M. C. Good. Redefinition of the robot motion control problem: Effects of plant dynamics, drive system constraints, and user requirements. Proc. Of 23rd IEEE Conf. on. Decision and Control, Las Vegas 724-730 (1984)

 

[2] M. W. Spong. Modeling and control of elastic joint manipulators. J. Dyn. Sys., Meas., Contr 109: 310-319 (1987)

 

[3] M. W. Spong, K. Khorasani, P. V. Kokotovic. An integral manifold approach to the feedback control of flexible joint robots. IEEE J. Robot. Autom. RA-3: 291–300 (1987)

 

[4] W. P. Li, B. Luo, H. Huang. Active vibration control of Flexible Joint Manipulator using Input Shaping and Adaptive Parameter Auto Disturbance Rejection Controller. Journal of Sound and Vibration 363: 97–125 (2016)

 

[5] A. M. Annaswamy, J. E. Wong. Adaptive control in the presence of saturation nonlinearity. International Journal of Adaptive Control and Signal Processing 11: 3-19 (1997)

 

[6] J-Z. Xiao, H-R. Wang, W. Zhang, H-R.Wei. Adaptive robotic control based on a Filter function under the saturation of actuators. Intentional Conference on Machine Learning and Cybernetics 283-287 (2006)

 

[7] W. E. Dixon. Adaptive regulation of amplitude limited for robot manipulators with uncertain kinematics and dynamics. IEEE Trans. on Automatic Control 52: 488-493 (2007)

 

[8] F. Ghorbel, J. Y. Hung, M. W. Spong. Adaptive control of flexible joint manipulators. IEEE Control System Magazine 9: 9-13 (1989)

 

[9] S. K. Spurgeon, L. Yao, X. Y. Lu. Robust tracking via sliding mode control for elastic joint manipulators. Proc. Inst. Mech. Eng. I 215: 405-417 (2001)

 

[10] A. Izadbakhsh. Robust adaptive control of voltage saturated flexible joint robots with experimental evaluations. AUT Journal of Modeling, and simulation 50: 31-38 (2018)

 

[11] A. Izadbakhsh. Closed-form dynamic model of Puma560 robot arm. Proceedings of the 4th International Conf. on Autonomous Robots and Agents 675-680 (2009)

 

 

[13] A. Izadbakhsh, A. Akbarzadeh Kalat, M. M. Fateh, S.M.R. Rafiei. A robust Anti-Windup control design for electrically driven robots-Theory and Experiment. International Journal of Control. Automation, and Systems 9: 1005-1012 (2011)

 

[14] A. Izadbakhsh, P. Kheirkhahan. An alternative stability proof for "Adaptive Type-2 fuzzy estimation of uncertainties in the control of electrically flexible-joint robots". Journal of Vibration and Control 25: 977–983 (2019)

 

[15] A. Izadbakhsh, S. Khorashadizadeh, P. Kheirkhahan. Real-time Fuzzy Fractional-order control of electrically driven flexible-joint robots. AUT Journal of Modeling, and simulation. DOI:10.22060/MISCJ.2018.13523.5075 (2018).

[16] S. Puga-Guzman, J. Moreno-Valenzula, V. santibanez. Adaptive neural network motion control of manipulators with experimental evaluations. The Scientific World Journal. Doi: 10.1155/2014/694706.

 

[17] H. Liu, T. Zhang. Adaptive Neural Network Finite-Time Control for Uncertain Robotic Manipulators. Journal of Intelligent & Robotic Systems 75: 363-377 (2014)

 

[18] A. Izadbakhsh, S. M. R. Rafiei. Endpoint Perfect Tracking Control of Robots – A Robust Non Inversion-Based Approach. International Journal of Control, Automation, and Systems 7: 888-898 (2009)

 

[19] A. Izadbakhsh, S. Khorashadizadeh, P. Kheirkhahan. Tracking control of electrically driven robots using a model free observer. Robotica 37: 729-755 (2019)

 

[20] A. Izadbakhsh. A note on the "nonlinear control of electrical flexible-joint robots". Nonlinear Dynamics 89: 2753-2767 (2017)

 

[21] A. Izadbakhsh. FAT-based robust adaptive control of electrically driven robots without velocity measurements. Nonlinear Dynamics 89: 289-304 (2017)

 

[22] A. Izadbakhsh, P. Kheirkhahan. Adaptive Fractional-Order Control of electrical Flexible-Joint Robots: Theory and Experiment. Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering, DOI: 10.1177/0959651818815384.

 

[23] P. Kokotovic, H. K. Khalil, J. O'reilly. Singular perturbation methods in control: analysis and design. Siam, Philadelphia (1999)