Dual-Axial Motion Control of a Magnetic Levitation System Using Hall-Effect Sensors
Bibliographic record
Abstract
This paper presents a new methodology to determine the position of a magnetically guided robot (MGR) in horizontal planes using magnetic flux sensors. This position determination methodology can be used independently as well as in collaboration with optical sensors in the case of the optical blockage. A combination of linear Hall-effect sensors (two sensors for each axis of motion) was employed to measure the magnetic flux in the MGR's working space. A configuration of several electromagnets was used as a source of magnetic field, and an analytical model of the system is developed. The MGR's position was determined based on the polynomial relation between the Hall-effect sensors' output and the location of the minimum magnetic potential energy point in horizontal planes. Using the cross-validation method, it was found that a fourth-order polynomial model could accurately predict the MGR's position. Experiments were conducted on a horizontal plane to validate the performance of position estimation using the magnetic flux sensing method. The accuracy of the position determination method was 0.4-mm root-mean-square errors in both thex- andy-direction over 8 × 8 mm2working area. This paper also experimentally validates a combined optical-magnetic position determination technique for the motion control of a magnetically guided robot in optical blockage conditions as unknown environment that can be used as a promising replacement of X-ray and ultrasound techniques.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".