🤖 AI Summary
To address degraded tracking performance of mechanical systems—such as exoskeletons—under unknown dynamic disturbances, this paper proposes a novel disturbance observer framework that jointly optimizes estimation speed and uncertainty quantification. We theoretically reveal an inherent trade-off between estimation responsiveness and uncertainty in disturbance reconstruction, and accordingly design two observers: the Interacting Multiple Model Extended Kalman Filter (IMM-EKF) and the Multi-Kernel Correntropy Extended Kalman Filter (MKCE-EKF). The IMM-EKF achieves adaptive model-set switching to accommodate time-varying interaction forces, while the MKCE-EKF employs an information-entropy-driven covariance adaptation mechanism to enhance robustness against non-Gaussian uncertainties. Experimental validation on a lower-limb exoskeleton demonstrates significant improvements: hip joint tracking errors are reduced by 36.3% and 16.2%, and knee joint errors by 46.3% and 24.4%, respectively—both metrics outperforming conventional EKF-based methods.
📝 Abstract
The nominal performance of mechanical systems is often degraded by unknown disturbances. A two-degree-of-freedom control structure can decouple nominal performance from disturbance rejection. However, perfect disturbance rejection is unattainable when the disturbance dynamic is unknown. In this work, we reveal an inherent trade-off in disturbance estimation subject to tracking speed and tracking uncertainty. Then, we propose two novel methods to enhance disturbance estimation: an interacting multiple model extended Kalman filter-based disturbance observer and a multi-kernel correntropy extended Kalman filter-based disturbance observer. Experiments on an exoskeleton verify that the proposed two methods improve the tracking accuracy $36.3%$ and $16.2%$ in hip joint error, and $46.3%$ and $24.4%$ in knee joint error, respectively, compared to the extended Kalman filter-based disturbance observer, in a time-varying interaction force scenario, demonstrating the superiority of the proposed method.