Adaptive Human–Robot Interaction Torque Estimation With High Accuracy and Strong Tracking Ability for a Lower Limb Rehabilitation Robot

Abstract

Accurate acquisition of interactive information is crucial for the effective execution of rehabilitation training. However, due to model and sensor errors, it is difficult to obtain interactive information accurately and quickly. To overcome these challenges, a novel accurate and fast estimation method for the human–robot interaction torques (HRITs) is proposed in this article. First, the HRIT model with order adaptive adjustment ability (HMOAA) is constructed. The polynomial order of HMOAA can be adaptively adjusted based on the partial state estimation, which is more consistent with the dynamic time-varying characteristics of HRIT. Second, the Sage–Husa adaptive strong tracking Kalman filter (SHASTKF) is designed based on the modified Sage–Husa adaptive Kalman filter (SHAKF) and strong tracking Kalman filter (STKF). The SHASTKF can quickly track the abrupt HRIT changes when the subject suddenly exerts active torques in rehabilitation training. Moreover, it also has the ability to recursively estimate the noise characteristics, and can stably complete the HRIT estimation task when the noise characteristics are unknown. Finally, simulations and experiments are conducted to validate the proposed method, and the comparison results demonstrate that the proposed method has good torque estimation precision and fast tracking ability of abrupt changes in HRITs.