Composite Locally Weighted Learning Position and Stiffness Control of Articulated Soft Robots With Disturbance Observers
Zhigang Zou, Zhiwen Li, Weibing Li, Yongping Pan
Abstract
Articulated soft robots (ASRs) driven by variable stiffness actuators (VSAs) are challenging to control well due to their highly nonlinear dynamics and difficulties in accurate modeling. The paper proposes a locally weighted learning (LWL)-based robust composite learning control (RCLC) solution for ASRs with agonistic-antagonistic (AA)-VSAs to enable the favorable tracking of both joint position and stiffness without exact robot models. In our solution, two LWL models are adopted online to estimate uncertainties in the link-side and stiffness dynamics, respectively, a nonlinear disturbance observer (DOB) is applied to improve tracking robustness at the link side, and a composite learning law is developed to achieve parameter convergence under a condition of interval excitation strictly weaker than persistent excitation so as to improve online modeling speed and accuracy. A distinctive feature of the proposed LWL-RCLC framework lies in the fact that the estimation of the DOB and the learning of LWL are independent yet work in a synergistic manner, which enables exact robot modeling online while improving tracking robustness. Experiments on a multi-DoF ASR with AA-VSAs have verified the superiority of the proposed method.
BibTeX
@inproceedings{iros2025_compositelocally,
title = {Composite Locally Weighted Learning Position and Stiffness Control of Articulated Soft Robots With Disturbance Observers},
author = {Zhigang Zou and Zhiwen Li and Weibing Li and Yongping Pan},
booktitle = {IROS 2025},
year = {2025}
}