Magneto-Rheological actuators for haptic devices: Design, modeling, control, and validation of a prototype clutch

Abstract

In our previous work [1], the potential benefits of Magneto-Rheological Fluid based actuators to the field of haptics were studied. Our results showed that the superior mechanical attributes of such actuators contribute to improvement of stability and transparency in haptic devices. To this end, a novel design of a small-scale MRF-based clutch, was proposed in [1]. This paper reports on the development and validation of the proposed MRF-based clutch. In addition, a closed-loop torque control strategy is presented. The feedback signal used in this control scheme comes from the magnetic field measurement and is used to compensate for the nonlinear behavior using an estimated model, based on Artificial Neural Networks (ANNs). Such a control strategy eliminates the need for torque sensors for providing feedback signals. The performance of the developed design and the effectiveness of the proposed modeling and control techniques are experimentally validated. The results clearly demonstrate that the clutch shows great potential for use in a multiple degrees-of-freedom (DOF) haptic interface for a class of medical applications.The authors are with the

The Recent Advances in Magnetorheological Fluids-Based Applications

“…Another important MRF application is intelligent clutches [35,36] that provide a wide torque transmissibility range upon the applied magnetic field. The long-term Figure 14.…”

A hybrid actuator with hollowed multi-drum magnetorheological brake and direct-current micromotor for hysteresis compensation

“…The experiments indicated the adaptive model could predict the behavior of the MR actuator more precisely than the Preisach model. Neural networks (Najmaei et al, 2015a;Zakerzadeh et al, 2011) are also efficient approaches in the hysteresis modeling. Other approaches include the resistor-capacitor operatorbased hysteresis modeling (Bai et al, 2019) and shape function-and memory mechanism-based hysteresis modeling (Chen et al, 2018).…”