Passive iFIR filters for data-driven velocity control in robotics
arXiv:2603.29882v1 Announce Type: new 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.
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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.
Subjects:
Robotics (cs.RO); Systems and Control (eess.SY)
Cite as: arXiv:2603.29882 [cs.RO]
(or arXiv:2603.29882v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2603.29882
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yi Zhang [view email] [v1] Tue, 31 Mar 2026 15:34:36 UTC (7,055 KB)
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