Velocity Estimation by Using Position and Acceleration Sensors
Why this work is in the frame
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Bibliographic record
Abstract
Knowledge of velocity is crucial to certain industrial applications involving high-bandwidth modeling and control. In conventional approaches, the velocities obtained from encoders or tachometers are quite noisy, and low-pass filters are usually engaged to generate usable velocity signals. The low-pass filter, however, causes significant phase lag that can severely affect both modeling and control accuracy in the mid- and high-frequency ranges. In this paper, two approaches using a combination of an encoder and an imperfect accelerometer are proposed to estimate velocities with high bandwidth. The two approaches, namely the two-channel approach and the observer-based approach, estimate velocities by applying proper frequency weightings to the encoder and accelerometer signals. The encoder mainly contributes to the low-frequency components of velocity estimation, and the accelerometer mainly contributes to the high-frequency components of velocity estimation. An adaptive mechanism for estimating the accelerometer gain is also presented. The effectiveness of the two velocity estimation approaches is verified experimentally with respect to a one-degree-of-freedom robot performing both rigid contact modeling and control. Extension to 3-D applications is discussed.
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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