Adaptive Control based Friction Estimation for Tracking Control of Robot Manipulators
<|reference_start|>Adaptive Control based Friction Estimation for Tracking Control of Robot Manipulators: 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.<|reference_end|>
@article{huang2024adaptive,
    title={Adaptive Control based Friction Estimation for Tracking Control of Robot
  Manipulators},
    author={Junning Huang, Davide Tateo, Puze Liu, Jan Peters},
    journal={arXiv preprint arXiv:2409.05054},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.05054},
    primaryClass={cs.RO}
}