Passive iFIR filters for data-driven velocity control in robotics

• URL: http://arxiv.org/abs/2603.29882v1
• Date: Tue, 31 Mar 2026 15:34:36 GMT
• Title: Passive iFIR filters for data-driven velocity control in robotics
• Abstract summary: We present a passive, data-driven velocity control method for nonlinear robotic manipulators.<n>Using only three minutes of probing data, a VRFT-based design identifies passive iFIR controllers.
• Score: 2.8431237407876258
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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