Stable Trajectory Tracking of Magnetic Swarms Under Uncertain Viscosity: An Adaptive Robust Lyapunov Redesign
Bibliographic record
Abstract
Magnetic microrobotic swarms are increasingly studied for their potential in precise motion control under complex and uncertain environments. One of the core challenges in swarm-level control lies in achieving accurate trajectory tracking in the presence of time-varying dynamic parameters, such as fluid resistance. To address such problem, we incorporate error integration into the swarms control framework and derive a kinematic model through model transformation. Based on this model, a Lyapunov-based adaptive robust control strategy is developed to ensure closed-loop stability and compensate for environmental uncertainties. The proposed controller dynamically adapts to unknown environmental variations, and rigorous theoretical analysis establishes the asymptotic stability of the system. A series of experiments are conducted on an electromagnetic actuation platform using silicone oil environments with different viscosities (5 cSt, 10 cSt, and 15 cSt) as well as under a rapidly varying viscosity generated by syringe pump to validate the method. Experimental results confirm that the proposed strategy enables the microrobotic swarm to achieve stable and precise trajectory tracking under varying resistance conditions, demonstrating its robustness and adaptability.
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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 | 0.001 |
| 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".