Adaptive Force Tracking Control of a Magnetically Navigated Microrobot in Uncertain Environment
Bibliographic record
Abstract
Magnetic navigation microrobotics is a promising technology in micromanipulation and medical applications. A magnetically navigated microrobot (MNM) usually has permanent magnets or ferromagnetic materials attached to it to create interaction force for navigation in the presence of an external magnetic field. During the exploration of the MNM, it is necessary to simultaneously control the position of the MNM and the contact force when the microrobot is constrained by its environment. However, owing to the small size of an MNM and noncontact property of magnetic levitation, installing on-board force sensors is very challenging. This paper presents a dual-axial interaction force determination mechanism that uses magnetic flux measurement, with no need for a conventional on-board force sensor. The interaction force is then used as the feedback force of a position-based impedance controller to actively track the reference force on the MNM in uncertain environment. To reduce the force tracking error caused by environmental uncertainty, an adaptive control algorithm is implemented to generate a reference motion trajectory that attempts to minimize the force error to an acceptable level. The force tracking performance of the robot is experimentally validated. A 2.01 μN root mean square force tracking error is reported. The proposed technique can be applied to biomedical microsurgery, such as for cutting tissue with controlled force.
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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) | 0.001 | 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 | 0.000 |
| 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 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".