Passive iFIR filters for data-driven velocity control in robotics

2026-03-31

AI Summary

This work addresses the lack of stability guarantees in data-driven velocity control for nonlinear robotic systems by proposing a virtual reference feedback tuning (VRFT) method augmented with passivity constraints. Requiring only three minutes of exploratory data, the approach designs an implicit finite impulse response (iFIR) controller that achieves stable velocity tracking in both joint and Cartesian spaces. Experimental validation on a Franka Emika Panda robot demonstrates up to a 74.5% reduction in Cartesian velocity tracking error. In contrast to conventional learning-based controllers, the proposed framework uniquely combines data-driven learning capability with theoretical closed-loop stability guarantees. Furthermore, it enables rapid relearning following dynamic changes, overcoming a key limitation of existing methods that struggle to simultaneously ensure high performance and stability.

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.

Problem

• velocity control
• data-driven control
• passivity
• robotic manipulators
• tracking performance

Innovation

• passive iFIR
• data-driven control
• VRFT
• velocity control
• passivity-based stability