Publikation
Adaptive Control based Friction Estimation for Tracking Control of Robot Manipulators
Junning Huang; Davide Tateo; Puze Liu; Jan Peters
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2409.05054, Pages 1-8, arXiv, 2024.
Zusammenfassung
Adaptive control is often used for friction com-
pensation 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. Additional
experiments can be found in: https://sites.google.
com/view/ral-friction-estimation/home.