Adaptive control based friction estimation for tracking control of robot manipulators
Huang, Junning ; Tateo, Davide LU
; Liu, Puze
and Peters, Jan
(2025)
In IEEE Robotics and Automation Letters
10(3).
p.2454-2461
- 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... (More)
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.
(Less)
- author
- Huang, Junning
; Tateo, Davide
LU
; Liu, Puze
and Peters, Jan
- publishing date
- 2025
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Calibration and identification, Model learning for control, Robust/adaptive control
- in
- IEEE Robotics and Automation Letters
- volume
- 10
- issue
- 3
- pages
- 8 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- scopus:85215438487
- ISSN
- 2377-3766
- DOI
- 10.1109/LRA.2025.3530159
- language
- English
- LU publication?
- no
- id
- 612bd496-ee3c-4998-907f-c616a2b52040
- date added to LUP
- 2025-10-16 14:09:33
- date last changed
- 2025-11-03 16:17:54
@article{612bd496-ee3c-4998-907f-c616a2b52040,
abstract = {{<p>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.</p>}},
author = {{Huang, Junning and Tateo, Davide and Liu, Puze and Peters, Jan}},
issn = {{2377-3766}},
keywords = {{Calibration and identification; Model learning for control; Robust/adaptive control}},
language = {{eng}},
number = {{3}},
pages = {{2454--2461}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{IEEE Robotics and Automation Letters}},
title = {{Adaptive control based friction estimation for tracking control of robot manipulators}},
url = {{http://dx.doi.org/10.1109/LRA.2025.3530159}},
doi = {{10.1109/LRA.2025.3530159}},
volume = {{10}},
year = {{2025}},
}