磁流变力矩执行器实现高精度扭矩控制，适合人机协作场景。
Magnetically Self-Sealed MR Haptic Actuator With PWM-Based Excitation and High-Fidelity Torque Control
- 采用磁自密封结构与PWM激励，降低迟滞并提升响应精度。
- 10kHz高频下扭矩响应非线性可控，实时控制器显著减少误差。
- 长时间运行温升仅2.5℃，适合持续稳定的人机交互应用。
精确稳定的扭矩呈现对安全、感知良好的人机交互至关重要。基于磁流变液（MRF）的执行器具有结构紧凑、响应迅速的优点，但实际应用需解决可靠的流体密封、低迟滞激励、精准扭矩控制及长时间稳定运行问题。本文提出一种集成MRF触觉系统，包含紧凑型磁自密封旋转执行器、低迟滞PWM驱动、高保真模型化扭矩渲染及长期运行稳定性。磁静力学仿真优化磁性与非磁性材料布局，使磁通聚焦于多盘扭矩区与永磁密封区，实现最大600 N·mm/A输出。实验表明，更高PWM频率可降低迟滞并改善重复性；在10 kHz时，响应由随扭矩变化方向与速度而变的非线性模型描述。实时控制器融合前馈、迟滞补偿、PI反馈与滑模校正，相比传统PID，方波响应的超调、欠调和稳态均方根误差分别降低77.4%、61.9%、68.3%。系统可跟踪正弦波与生物力学模型参考信号，1.5小时测试中线圈附近温升仅2.5℃，无明显追踪性能下降。该高保真扭矩渲染将从根本上推动人机协作的安全性、效率与直观性提升。
原文摘要 · Abstract (English)
Accurate and stable torque rendering is essential for safe and perceptive human--machine interaction. Magnetorheological fluid (MRF)-based actuators offer a compact and rapidly controllable solution for haptic feedback, but their practical implementation requires reliable fluid sealing, low-hysteresis excitation, accurate torque control, and stable long-duration operation. This article presents an integrated MRF haptic system featuring a compact magnetically self-sealed rotary actuator, low-hysteresis PWM operation, high-fidelity model-based torque rendering, and stable performance during long-time operation. Magnetostatic simulation guides the arrangement of magnetic and nonmagnetic materials to focus flux in the multidisk torque and permanent-magnet sealing regions, enabling a maximum 600 N$\cdot$mm/A output. Experiments show that higher PWM frequencies reduce hysteresis and improve repeatability. At 10 kHz, the response is represented by a nonlinear model that varies with the direction and speed of torque change. The real-time controller combines feedforward, hysteresis compensation, PI feedback, and sliding-mode correction. Compared with PID, it reduces square-wave overshoot, undershoot, and steady-state RMSE by 77.4\%, 61.9\%, and 68.3\%, respectively. It tracks sinusoidal and biomechanics-model-based references, and a 1.5-h test shows only a 2.5 $^\circ$C rise near the coil with no clear tracking loss. This high-fidelity torque rendering will fundamentally transform human--robot collaboration by making interactions safer, more efficient, and more intuitive.
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