Reciprocating Actuation Utilizing Two Coaxial Electrorheological Clutches

Summary

Instead of directly controlling a driven motor to achieve high-speed reciprocating motion, employing clutches for bidirectional actuation presents a compelling alternative. By decoupling the motor from dynamic load reversals and enabling relatively steady unidirectional operation, mechanical and thermal stresses on the motor can be significantly reduced, thereby improving system reliability and extending operational lifespan. Furthermore, by eliminating motor reversals and introducing antagonistic driving, effects of gear backlash can be mitigated, leading to improved positioning accuracy and enhanced overall system performance. Considering various advantages of ER fluid clutches, we designed a linear actuation system to achieve high-speed linear reciprocating motion utilizing two coaxial ER fluid clutches, as shown in Fig.1.

As the first step, we focused on modeling the transmission torque of two coaxial electrorheological (ER) fluid clutches through a data-driven approach. Instead of simplifying the viscosity term in the Bingham model to be a constant as shown in conventional methods, we propose the method of introducing electric field-dependent nonlinearity into the viscosity term to better capture the complex rheological behavior of ER fluids. Based on this framework, we developed a heuristic explicit model (HEM) and a radial basis function model (RBFM) that incorporate the mechanical characteristics of the coaxial clutch structure. Furthermore, we explored direct estimation methods using a radial basis function network (RBFN) and a feedforward neural network (FNN) without relying on the Bingham model. Comparative evaluations with traditional ER models validated the effectiveness of our nonlinear formulations. Notably, the FNN approach demonstrated superior accuracy (as shown in Fig.2) even with a single hidden layer containing only a few neurons, making it well-suited for real-time implementation with minimal computational overhead. Real-time validation across diverse operating conditions further confirmed the feasibility and robustness of the FNN-based method.

Next, we targeted on achieving accurate position control of the proposed system. In order to realize real-time application, a simple feedforward neural network with a single hidden layer was proposed to model the inverse problem of torque transmission. We then developed the position controller based on the computed torque control (CTC) framework, augmented with a proportional–derivative (PD) feedback loop. As shown in Fig.3, experimental results confirmed that the proposed control architecture delivers robust and accurate position control, even under disturbances caused by randomly changing motor torque inputs.

Fig.1 The developed system

Fig.2 Result of torque transmission estimation

Fig.3 Result of position tracking control

Reference

1. S. Huang and M. Ishikawa: Torque Transmission Modeling of Two Coaxial Electrorheological Clutches for Reciprocating Actuation. IEEE Robotics and Automation Letters,vol.10, no.12,pp.12724-12731, 2025. doi:10.1109/LRA.2025.3625469
2. S. Huang and M. Ishikawa: Reciprocating Motion Utilizing Electrorheological Fluid Clutches: Position Control based on a Neural Network Model and the PD Computed Torque Control Method. Journal of the Robotics Society of Japan (to appear)