Force adaptation with recursive regression Iterative Learning Controller
Bojan Nemec, Tadej Petrič, Aleš Ude
Abstract
In this paper we exploit Iterative Learning Controllers (ILC) schemes in force adaptation tasks. We propose to encode the control signal with Radial Basis Functions (RBF), which enhances the robustness of the ILC scheme and allows to vary the execution speed of the learned motion. For that a novel control scheme is proposed, which updates the feedforward compensation signals based on current iteration cycle signals in contrast to the standard ILC, which uses signals from the previous iteration cycle. This reduces the computational burden and enhances the adaptation speed. Stability of the proposed control law is analysed and discussed. The proposed approach is evaluated in simulation and on a Kuka Light Weight Robot Arm where the task was to perform force-based surface following with both discrete and periodic movements.
BibTeX
@inproceedings{iros2015_forceadaptationw,
title = {Force adaptation with recursive regression Iterative Learning Controller},
author = {Bojan Nemec and Tadej Petrič and Aleš Ude},
booktitle = {IROS 2015},
year = {2015}
}