Extended-state observer control with online payload identification for HRI in series elastic actuators

Abstract

Safe human–robot interaction requires actuators that combine compliance with accurate force inference. Series elastic actuators (SEAs) are particularly suitable for this purpose; however, separating payload-induced torques from voluntary human interaction forces without dedicated sensors remains a critical challenge. This paper proposes a unified observer-based control framework for SEAs that integrates three key components: a linear parameter-varying extended state ob server (LPV-ESO), an online algebraic payload estimator, and a disturbance driven motion intention identification (MII) mechanism. The payload estimator continuously updates the observer gains to adapt to load variations, while the LPV-ESO decouples payload dynamics from human interaction forces. The dis turbance estimate is then processed through the MII block to infer operator intent in real time without additional sensing hardware. Numerical simulations under nominal conditions and ±10% parametric uncertainty confirm bounded trajec tory tracking error, rapid convergence of payload estimation, and effective sepa ration of payload-induced torques from voluntary interaction forces. The results demonstrate that the proposed framework achieves sensorless force decompo sition and reliable motion intention inference, offering a practical solution for SEA-based collaborative robots in rehabilitation, assistance, and industrial ap plications.