Passive iFIR filters for data-driven velocity control in robotics

TL;DR

This paper introduces a passive, data-driven velocity control method for robotic manipulators that outperforms traditional PID controllers in tracking accuracy while ensuring stability through passivity constraints.

Contribution

It presents a novel VRFT-based design of passive iFIR controllers that achieve better tracking performance with minimal data and maintain stability even with changing robot dynamics.

Findings

Achieves up to 74.5% reduction in Cartesian velocity tracking error.

Uses only three minutes of probing data for controller design.

Maintains stability through passivity constraints despite data-driven learning.

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.