Passive iFIR filters for data-driven velocity control in robotics
This work addresses the problem of stable and high-performance velocity control for robotic manipulators, bridging learning-based control with stability guarantees, though it is incremental as it builds on existing VRFT and passivity methods.
The paper tackles velocity control for nonlinear robotic manipulators by introducing a passive, data-driven method that uses only three minutes of probing data to design controllers, achieving up to a 74.5% reduction in tracking error compared to a PID baseline on a Franka Research 3 robot.
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.