Torque Transmission Modeling of Two Coaxial Electrorheological Clutches for Reciprocating Actuation

Abstract

This study focuses 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 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. These findings contribute new insights into ER fluid applications.