Improved Extended Kalman Filter-Based Disturbance Observers for Exoskeletons
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908599504076800 |
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| author | Li, Shilei Shi, Dawei Iwasaki, Makoto Ning, Yan Zhou, Hongpeng Shi, Ling |
| author_facet | Li, Shilei Shi, Dawei Iwasaki, Makoto Ning, Yan Zhou, Hongpeng Shi, Ling |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_15533 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Improved Extended Kalman Filter-Based Disturbance Observers for Exoskeletons Li, Shilei Shi, Dawei Iwasaki, Makoto Ning, Yan Zhou, Hongpeng Shi, Ling Robotics 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. |
| title | Improved Extended Kalman Filter-Based Disturbance Observers for Exoskeletons |
| topic | Robotics |
| url | https://arxiv.org/abs/2510.15533 |