Magnetically Self-Sealed MR Haptic Actuator With PWM-Based Excitation and High-Fidelity Torque Control
Authors: Dong Qiang, Tian Yuan, Song Yang, Kequan Xia, Thomas Reddyhoff, Yikun Zhang, Cheng Cheng, Min Yu
Organizations: Department of Mechanical Engineering, Imperial College London, London SW7 2AZ, U.K. · School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
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⋅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 ∘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.
Figures & tables
Fig. 1: Representative application example of the proposed MRF rotary haptic actuator in robot-assisted surgery. The operator manipulates the user-side haptic interface to generate motion commands for the surgical robot, which reproduces the commanded motion in the task environment and performs tool–tissue interaction. The resulting interaction force/torque signal is transmitted back to the user-side interface and rendered by the MRF actuator as controllable resistive torque, thereby providing the operator with real-time haptic feedback during manipulation.
Fig. 2: MRF haptic actuator architecture and operating principle of the proposed magnetically self-sealed MRF haptic actuator. (a) Overall sectional view of the integrated rotary actuator. (b) Exploded view of the prototype assembly. (c) Enlarged sealing region, where the permanent magnet forms a dedicated sealing flux path and the flux-focusing teeth locally enhance the magnetic field for MRF sealing. (d) Enlarged working region enabling efficient field-dependent modulation of the magnetorheological effect for controllable torque generation. (e) Torque-feedback mechanism of the rotary haptic interface. (f) PWM-controlled MRF shear mechanism, in which the drive current excites the coil, the resulting magnetic field induces particle-chain structures across the shear gap, and the resistance of these structures to relative disk motion produces a continuously adjustable resistive torque. The sealing and working regions are designed as magnetically decoupled functional zones, so that the sealing field stabilizes the MRF distribution without significantly disturbing the controllable torque-generation region.
Symbol
Parameter
Value
Nc
Coil turns
1000
Ds
Main shaft diameter
6 mm
Dj
Actuator outer diameter
68.4 mm
Hj
Actuator height
19.8 mm
gw
MRF working gap
0.2 mm
gs
Seal gap
0.1 mm
TABLE I: Key design parameters of the proposed MRF haptic actuator
Fig. 3: Magnetostatic field distribution of the proposed MRF haptic actuator under a coil current of 500 mA. The magnetic flux is concentrated primarily in the disk-based working region and the peripheral sealing regions, supporting controllable torque generation and magnetically assisted fluid sealing, respectively.
Fig. 4: (a) Experimental setup for open-loop characterisation and closed-loop torque-rendering experiments. (b) Signal flow for characterisation and control of the proposed MRF haptic actuator. The test rig integrates motor-driven rotary excitation, torque and encoder measurements, PWM-based power driving, and FPGA–RT real-time implementation.
Fig. 5: Open-loop characterization of the proposed MRF haptic actuator. (a) PWM-frequency-dependent torque–input hysteresis loops and extracted hysteresis metrics. (b) Hysteresis dependence on input frequency under the selected 10 kHz PWM regime. (c) Hysteresis dependence on operating range under the selected 10 kHz PWM regime. (d) Feature extraction and control-oriented model construction under the selected 10 kHz PWM regime.
Fig. 6: System architecture of the proposed MRF rotary haptic actuator for torque rendering in robot-assisted surgery. The operator commands the surgical robot through the user-side interface, while the resulting tool–tissue interaction force/torque is converted into a torque reference and tracked by the hierarchical controller. The MRF actuator renders the corresponding resistive torque to the operator, thereby closing the real-time haptic feedback loop.
Parameter
Value
Note
Ts
1.0×10−3s
Real-time sampling period
Tf,ref
2.0×10−3s
Reference-torque filter
Tf,meas
2.0×10−3s
Measured-torque filter
ηff
0.66
Feedforward scaling
ηhys
1.60
Hysteresis-compensation scaling
Tmin
8.25Nmm
Lower backbone bound
TABLE II: Implementation Parameters of the Proposed HHC Controller
Fig. 7: Closed-loop comparison of PID, FF–PI, and the proposed controller under representative torque-rendering tasks. (a) Large-amplitude square-wave response. (b) Repeated square-wave response. (c) Sinusoidal response. (d) Biomechanics-inspired interaction torque profile constructed from typical needle–tissue interaction phases [ 20 , 21 , 22 ] .
Reference input
Metric
PID
FF–PI
Proposed HHC
Reduction vs. PID
Reduction vs. FF–PI
Square-wave references †
RMSE [N mm]
34.69
32.54
31.32
9.7%
3.7%
Avg. response time [s]
0.102
0.082
0.080
21.9%
2.5%
Avg. overshoot [N mm]
22.35
19.75
5.05
77.4%
74.4%
Avg. undershoot [N mm]
19.53
21.30
7.43
61.9%
65.1%
Steady-state RMSE [N mm]
6.39
4.68
2.02
68.3%
56.8%
Sinusoidal reference
Trajectory RMSE [N mm]
14.43
16.59
15.95
-10.5%
3.9%
TABLE III: Quantitative Closed-Loop Tracking Performance of Different Controllers
Fig. 8: Long-duration closed-loop stability test of the proposed MRF haptic actuator. (a) Torque tracking response and temperature variation during continuous operation. (b) Enlarged view of the selected tracking segment. (c) Thermocouple placement on the housing and top side for temperature monitoring.
Fig. 9: Haptic feedback validation based on in vivo validated biomechanical model of brain drug injection. (a) Schematic of localized brain drug injection and local pressure generation during injection. (b) Signal flow from biomechanical pressure modelling to MRF haptic torque rendering. (c) Closed-loop tracking results under three representative flow rate ( Qi ) and tissue permeability ( κi ) conditions, with the corresponding tracking errors shown below.
Contact-rich manipulation depends on force sensing that is hard to infer from visual signals alone, both for collecting demonstrations and for training policies. Force-annotated data, however, remains hard to obtain at scale: real-robot collection ties every demonstration to physical hardware, physics simulators report contact forces that deviate systematically from real measurements, and learned world simulators, though scalable and realistic, are vision-only, so operators feel nothing during data collection and the data carries no force/torque (F/T) labels. We present HapticWorld, an interactive world simulator that predicts joint torque together with observations and renders it back to the operator in real time, closing the haptic loop between a human and a learned world model. Across three contact-rich tasks, torque feedback raises data collection throughput by 1.6 times on average. Policies trained on HapticWorld-generated demonstrations succeed in 54/60 real-world trials, approaching the 56/60 upper bound of real-world data, and far exceeding the 19/60 success rate of the vision-only baseline. Moreover, the success rates measured inside HapticWorld closely match real-world evaluation, demonstrating that HapticWorld can serve as a stand-alone F/T-conditioned policy evaluation platform.
Shaoting Peng, Litian Liang, Yixuan Wang +4
Ant Group · Coordinated Science Laboratory, Electrical and Computer Engineering, University of Illinois Urbana-Champaign · NVIDIA +1
Direct torque sensing is a growing need in the robotics community to enable precise control and interactions where torque estimation from motor current is not sufficient. This paper presents a novel disk-shaped magnetoelastic torque sensor with a compact axial envelope of about 1 cm, suitable for integration in robotic joints. A four-magnetometer architecture is used to measure the field modulated by the stress affecting a narrow magnetized region while rejecting the effects of parasitic cross forces. A custom-designed magnetic shield enhances the torque sensitivity while reducing the external stray fields by a factor 7x. The device measures the torque with an accuracy of 1.34 %FS relative to the 50 Nm full scale (FS). The paper details the development of the sensor through the mechanical design, the magnetization procedure, and the experimental validation. The results demonstrate the potential of the proposed sensor for robotic applications.
Bruno Brajon, Enrico Gasparin, Nicole Yazigy +5
Melexis Technologies SA, Bevaix, Switzerland · CSEM SA, Neuchˆatel, Switzerland
Fiber-reinforced pneumatic twisted-and-coiled actuators (FR-PTCAs) offer high power density and compliance but their strong hysteresis and lack of intrinsic proprioception limit effective closed-loop control. This paper presents a self-sensing FR-PTCA integrated with a conductive nickel wire that enables intrinsic force estimation and indirect displacement inference via inductance feedback. Experimental characterization reveals that the inductance of the actuator exhibits a deterministic, low-hysteresis inductance-force relationship at constant pressures, in contrast to the strongly hysteretic inductance-length behavior. Leveraging this property, this paper develops a parametric self-sensing model and a nonlinear hybrid observer that integrates an Extended Kalman Filter (EKF) with constrained optimization to resolve the ambiguity in the inductance-force mapping and estimate actuator states. Experimental results demonstrate that the proposed approach achieves force estimation accuracy comparable to that of external load cells and maintains robust performance under varying load conditions.
Yunsong Zhang, Tianlin Li, Mingyang Yang +1
Robotics and Control Laboratory, School of Advanced Manufacturing and Robotics, and the State Key Laboratory of Turbulence and Complex Systems, Peking University, Beijing 100871, China