Modelling and adaptive dynamic sliding mode control of dielectrophoresis-based micromanipulation
Bibliographic record
Abstract
Automated, precise single particle manipulation in the microscale is in great demand and is one of the great challenges in biomedical and biochemical engineering. Automatic micromanipulation has also become a microrobotics challenge. Following this challenge, control technology is integrated with dielectrophoresis (DEP)-based micromanipulation technology in this paper to construct automatic DEP-based micromanipulation systems. DEP micromanipulation systems with electrodes of quadrupole polynomial geometry are developed as controllable microactuators. A semianalytical modelling method is proposed to formulate the analytical models of the DEP manipulation systems, which manifests that the DEP manipulation systems are non-affine non-linear systems. Then, taking the parameter uncertainties, unmodelled dynamics and external disturbances into account, an adaptive law combined with a dynamic sliding mode controller is designed for two-dimensional trajectory tracking control of a DEP micromanipulation system. The closed-loop system is proved stable in the presence of bounded lumped uncertainty based on the Lyapunov theorem. Finally, simulation results show the validity of the proposed control design.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".