{"ID":2854046,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.15533","arxiv_id":"2510.15533","title":"Improved Extended Kalman Filter-Based Disturbance Observers for Exoskeletons","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.","short_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 inher...","url_abs":"https://arxiv.org/abs/2510.15533","url_pdf":"https://arxiv.org/pdf/2510.15533v1","authors":"[\"Shilei Li\",\"Dawei Shi\",\"Makoto Iwasaki\",\"Yan Ning\",\"Hongpeng Zhou\",\"Ling Shi\"]","published":"2025-10-17T11:07:54Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}