Application of a Nonlinear Hammerstein-Wiener Estimator in the Development and Control of a Magnetorheological Fluid Haptic Device for Robotic Bone Biopsy
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Bibliographic record
Abstract
A force generator module (FGM) based on magnetorheological fluid (MRF) was developed to provide force-feedback information for applications in tele-robotic bone biopsy procedures. The FGM is capable of rapidly re-producing a wide range of forces that are common in bone biopsy applications. As a result of the nonlinear nature of MRF, developing robust controllers for these mechanisms can be challenging. In this paper, we present a case study motivated by robotic bone biopsy. We use a non-linear Hammerstein-Wiener (H-W) estimator to address this challenge. The case is presented through three studies. First, an experiment to develop design constraints is presented and describes biopsy force measurements for various animal tissues. Required output forces were found to range between <1 N and <50 N. A second study outlines the design of the FGM and presents the experimental characterization of the hysteretic behavior of the MRF. This data is then used as estimators and validators to develop the nonlinear Hammerstein-Wiener (H-W) model of the MRF. Validation experiments found that the H-W model is capable of predicting the behavior of the MRF device with 95% accuracy and can eliminate hysteresis in a closed-loop control system. The third study demonstrates the FGM used in a 1-DOF haptic controller in a simulated robotic bone-biopsy. The H-W control tracked the input signal while compensating for magnetic hysteresis to achieve optimal performance. In conclusion, the MRF-based device can be used in surgical robotic operations that require a high range of force measurements.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it