Passive iFIR filters for data-driven velocity control in robotics
Abstract
We present a passive, data-driven velocity control method for nonlinear robotic manipulators that achieves better tracking performance than optimized PID with comparable design complexity. Using only three minutes of probing data, a VRFT-based design identifies passive iFIR controllers that (i) preserve closed-loop stability via passivity constraints and (ii) outperform a VRFT-tuned PID baseline on the Franka Research 3 robot in both joint-space and Cartesian-space velocity control, achieving up to a 74.5% reduction in tracking error for the Cartesian velocity tracking experiment with the most demanding reference model. When the robot end-effector dynamics change, the controller can be re-learned from new data, regaining nominal performance. This study bridges learning-based control and stability-guaranteed design: passive iFIR learns from data while retaining passivity-based stability guarantees, unlike many learning-based approaches.
Keywords
Cite
@article{arxiv.2603.29882,
title = {Passive iFIR filters for data-driven velocity control in robotics},
author = {Yi Zhang and Zixing Wang and Fulvio Forni},
journal= {arXiv preprint arXiv:2603.29882},
year = {2026}
}