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.

翻译：我们提出了一种被动、数据驱动的速度控制方法，用于非线性机器人操作臂，相比优化后的PID控制，该方法在可比的设计复杂度下实现了更优的跟踪性能。仅利用三分钟探测数据，基于VRFT的设计即可识别出被动iFIR控制器，该控制器能够：（i）通过无源约束保持闭环稳定性；（ii）在Franka Research 3机器人的关节空间和笛卡尔空间速度控制中，均优于VRFT调谐的PID基线，在最苛刻参考模型的笛卡尔速度跟踪实验中，跟踪误差降低高达74.5%。当机器人末端执行器动力学发生变化时，控制器可通过新数据重新学习，恢复标称性能。本研究架起了学习控制与稳定性保证设计之间的桥梁：与许多基于学习的方法不同，被动iFIR在从数据学习的同时，保留了基于无源的稳定性保证。