用3分钟数据训练的被动滤波器，让机械臂速度控制更准且稳定。
Passive iFIR filters for data-driven velocity control in robotics
- 基于虚拟参考反馈的被动iFIR控制器，用数据驱动设计。
- 在Franka机器人上比优化PID减少74.5%轨迹误差。
- 动态变化后可快速重训练，适合需稳定性的实时控制场景。
我们提出一种被动、数据驱动的速度控制方法，用于非线性机械臂，其跟踪性能优于优化后的PID控制器，且设计复杂度相当。仅需三分钟探测数据，基于VRFT的方案即可设计出被动iFIR控制器，该控制器（一）通过被动性约束保证闭环稳定性；（二）在Franka Research 3机器人上，无论是关节空间还是笛卡尔空间的速度控制中，均优于VRFT调优的PID基线，在最严苛参考模型的笛卡尔速度追踪实验中，最大可将追踪误差降低74.5%。当机械臂末端动力学发生变化时，控制器可基于新数据重新学习，恢复到标称性能。本研究连接了数据驱动控制与稳定性保障设计：被动iFIR在从数据中学习的同时，仍保持基于被动性的稳定性保证，这不同于许多学习型方法。
原文摘要 · Abstract (English)
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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