MIMO regulation control design for magnetic steering of a ferromagnetic particle inside a fluidic environment
Bibliographic record
Abstract
As an important development of medical instrumentation, minimally invasive therapeutic operations have been recently introduced. The foremost element of minimally invasive techniques is navigating a micro-device through human body, especially inside blood vessels. A remote actuation over the micro-device is normally provided by electromagnetic actuators. In most applications, a control scheme is also required to initiate the actuation force, the magnetic propulsion, such that at every time step, the micro-device moves towards or along a given path. This paper contributes in development of the electromagnetic system model mostly used in magnetic navigation systems to be representable in control affine form. Next, a multi-input multi-output (MIMO) trajectory tracking controller is designed to conduct the auto-navigation of the device along a given path. This method is a generalised version of a ‘semi-global nonlinear output regulation’ introduced for single-input single-output (SISO) systems. Finally, the proposed scheme is examined for an iron particle moving in a fluidic environment. The simulation results show fast decay in deviation of the particle position from the reference path under some assumptions. This shows that the proposed scheme can be offered for medical applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".