Fractional‐order sliding mode control for a novel magneto‐electro‐elastic microtube robot
Bibliographic record
Abstract
Abstract In this paper, a tracking controller based on a non‐integer sliding surface is proposed for a magneto‐electro‐elastic (MEE) fluid‐conveying microtube robot. The smart/adaptive MEE material enables us to control the robot with no need for external sensors and actuators. The micro‐robot lateral motion is modeled by Euler–Bernoulli beam equations. The governing equation of the robot is derived using the constitutive equations of MEE materials and Maxwell's principle followed by Hamilton's variational method. Based on the extracted dynamic model, a novel non‐integer order sliding mode controller is introduced to suppress the microtube vibration and to provide robust path following for the robot tip. This control approach is compatible with the parameter‐varying nature of the robot dynamics. Theoretical analyses, based on Lyapunov theory, are also conducted to verify the stability of the closed‐loop system. Comparative simulations are finally performed to show the efficiency of the proposed system in comparison with the conventional micro tubes made of smart materials and with an integer order sliding mode controller (SMC). The results demonstrate that the proposed robot properly meets the performance requirements in terms of vibration suppression and trajectory tracking, even in the presence of disturbances.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".