Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 57 tok/s
Gemini 2.5 Pro 52 tok/s Pro
GPT-5 Medium 20 tok/s Pro
GPT-5 High 19 tok/s Pro
GPT-4o 93 tok/s Pro
Kimi K2 176 tok/s Pro
GPT OSS 120B 449 tok/s Pro
Claude Sonnet 4.5 35 tok/s Pro
2000 character limit reached

Adaptive Control based Friction Estimation for Tracking Control of Robot Manipulators (2409.05054v2)

Published 8 Sep 2024 in cs.RO

Abstract: Adaptive control is often used for friction compensation in trajectory tracking tasks because it does not require torque sensors. However, it has some drawbacks: first, the most common certainty-equivalence adaptive control design is based on linearized parameterization of the friction model, therefore nonlinear effects, including the stiction and Stribeck effect, are usually omitted. Second, the adaptive control-based estimation can be biased due to non-zero steady-state error. Third, neglecting unknown model mismatch could result in non-robust estimation. This paper proposes a novel linear parameterized friction model capturing the nonlinear static friction phenomenon. Subsequently, an adaptive control-based friction estimator is proposed to reduce the bias during estimation based on backstepping. Finally, we propose an algorithm to generate excitation for robust estimation. Using a KUKA iiwa 14, we conducted trajectory tracking experiments to evaluate the estimated friction model, including random Fourier and drawing trajectories, showing the effectiveness of our methodology in different control schemes.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (28)
  1. Input to state stability: Basic concepts and results. Nonlinear and optimal control theory: lectures given at the CIME summer school held in Cetraro, Italy June 19–29, 2004, pages 163–220, 2008.
  2. The generalized maxwell-slip model: a novel model for friction simulation and compensation. IEEE Transactions on automatic control, 50(11):1883–1887, 2005.
  3. Brian Armstrong-Helouvry. Control of machines with friction, volume 128. Springer Science & Business Media, 1991.
  4. Estimation of inertial parameters of manipulator loads and links. The International Journal of Robotics Research, 5(3):101–119, 1986.
  5. C Canudas-de Wit and P Lischinsky. Adaptive friction compensation with dynamic friction model. IFAC Proceedings Volumes, 29(1):2078–2083, 1996.
  6. Active disturbance rejection control of multi-joint industrial robots based on dynamic feedforward. Electronics, 8(5):591, 2019.
  7. A new model for control of systems with friction. IEEE Transactions on automatic control, 40(3):419–425, 1995.
  8. Residual model learning for microrobot control. In 2021 IEEE International Conference on Robotics and Automation (ICRA), pages 7219–7226. IEEE, 2021.
  9. Observer-based nonlinear compensation of friction in a positioning system. German-Polish Sem., Cologne, pages 1–10, 1997.
  10. Dynamic friction model with thermal and load dependency: modeling, compensation, and external force estimation. In 2019 International Conference on Robotics and Automation (ICRA), pages 7367–7373. IEEE, 2019.
  11. Model-free friction observers for flexible joint robots with torque measurements. IEEE Transactions on Robotics, 35(6):1508–1515, 2019.
  12. Friction estimation for tendon-driven robotic hands. In 2021 IEEE International Conference on Robotics and Automation (ICRA), pages 6505–6511. IEEE, 2021.
  13. Adaptive friction compensation in trajectory tracking control of dlr medical robots with elastic joints. In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 1149–1154. IEEE, 2012.
  14. Friction observer and compensation for control of robots with joint torque measurement. In 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 3789–3795. IEEE, 2008.
  15. Optimal excitation trajectories for mechanical systems identification. Automatica, 131:109773, 2021.
  16. High-gain pi-like control of euler–lagrange mechanical systems: Simulations and experiments in 2dof robotic manipulators. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 236(4):857–869, 2022.
  17. Observer-based friction compensation. In Proceedings of 35th IEEE Conference on Decision and Control, volume 4, pages 4345–4350. IEEE, 1996.
  18. Friction models and friction compensation. Eur. J. Control, 4(3):176–195, 1998.
  19. An adaptive friction compensator for global tracking in robot manipulators. Systems & Control Letters, 33(5):307–313, 1998.
  20. Observer-based compensation of discontinuous friction. In 2004 43rd IEEE Conference on Decision and Control (CDC)(IEEE Cat. No. 04CH37601), volume 5, pages 4940–4945. IEEE, 2004.
  21. End-to-end learning of hybrid inverse dynamics models for precise and compliant impedance control. arXiv preprint arXiv:2205.13804, 2022.
  22. On the adaptive control of robot manipulators. The international journal of robotics research, 6(3):49–59, 1987.
  23. Parameter identification of the kuka lbr iiwa robot including constraints on physical feasibility. IFAC-PapersOnLine, 50(1):6863–6868, 2017.
  24. Optimal robot excitation and identification. IEEE transactions on robotics and automation, 13(5):730–740, 1997.
  25. Adaptive friction compensation: a globally stable approach. IEEE/ASME Transactions on Mechatronics, 21(1):351–363, 2015.
  26. Scipy 1.0: fundamental algorithms for scientific computing in python. Nature methods, 17(3):261–272, 2020.
  27. Linear matrix inequalities for physically consistent inertial parameter identification: A statistical perspective on the mass distribution. IEEE Robotics and Automation Letters, 3(1):60–67, 2017.
  28. Extending a dynamic friction model with nonlinear viscous and thermal dependency for a motor and harmonic drive gear. In 2018 IEEE International Conference on Robotics and Automation (ICRA), pages 783–790. IEEE, 2018.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up