Robust Friction Estimation for an Active Upper-Limb Exoskeleton Via SOSML Observer

Abstract

Friction in compact, geared actuators remains a primary barrier to transparency in upper-limb exoskeletons, especially near zero velocity and during frequent reversals. A momentum-based estimation framework is developed and evaluated on a two-DoF active device (modified EDUExo), where joint friction is recovered from on-board joint measurements and fitted to Coulomb–viscous and Stribeck laws. Two estimators are compared under identical conditions: a first-order momentum observer (FO) and a second-order sliding-mode momentum observer (SOSML). Three velocity trajectories are designed to probe complementary behaviors. In simulation, SOSML adheres more closely to the S-shaped friction law, and preserves loop symmetry under encoder noise; parameter variance and robustness under structured model mismatch are likewise improved relative to FO. The results indicate that SOSML delivers lower lag, cleaner noise profiles, and reduced parameter drift without changing the signal set or adding sensors, thereby strengthening friction identification and compensation on compact, gear-reduced actuators.