Improved Extended Kalman Filter-Based Disturbance Observers for Exoskeletons

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 tradeoff 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 (IMMEKF)-based disturbance observer (DOB) and a multikernel correntropy extended Kalman filter-based DOB (MKCEKF-DOB). Experiments on an exoskeleton verify that the proposed two methods improve the tracking accuracy by 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 DOB, in a time-varying interaction force scenario, demonstrating the superiority of the proposed methods.