ROBUST VIBRATION CONTROL FOR FLEXIBLE ARMS USING THE SLIDING MODE METHOD
Bibliographic record
Abstract
ABSTRACT The vibration control of flexible arms is accomplished here using the sliding mode method, where the traditional discontinuous approach is modified by a differentiable one. The higher order modes of the flexible arm are treated as disturbances and are compensated by introducing a disturbance observer. Simplified expressions of the motor angular and the strain moment for the flexible arm with a disturbance observer are obtained, where the remaining disturbance and the model uncertainties are considered as system uncertainties. The robustness of the sliding mode control is effectively employed to cope with the system uncertainties, where the bounds of the uncertainties are adaptively updated. The proposed control law simultaneously causes the motor angular to track a desired signal and the strain moment to approach zero. The stability of the controlled flexible arm is analyzed based on the obtained important fact that a part of the control input is the approximate estimate of a special signal generated by the uncertainty. The motor angular tracking error and the converging speed of the controlled signals are determined by means of design parameters. Experimental results demonstrate the robustness of the proposed method.
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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".