Mitigation of torsional vibration in large mill drive train system using state feedback control method
Bibliographic record
Abstract
Torsional vibration limits the speed loop response of industrial drives and servo systems, deteriorating the transient response to speed commands and load disturbances. This thesis presents a damping method for torsional vibration produced by compliant components between the motor and the load in rolling mill applications. The proposed damping algorithm can solve the limitation of the classical damping approaches due to the large values of system time delay. The proposed algorithm is based on State Feedback Control (SFC) method with modified Linear Quadratic Gaussian (LQG) approach using a torque sensor as a feedback element. The result of modification is a low order single-input single-output compensator that mitigates the torsional vibration without affecting the speed loop. The verification of the design and the dynamic performance is accomplished by using time and frequency domain responses with real rolling mill system parameters. The performance of step commands, mitigation of torsional vibration and robustness to parameter variation is satisfied by using the proposed method. Also disturbance rejection performance is improved through load torque compensation. The experiment is performed on a 0.8 KW system with 24 Hz mechanical resonant frequency. Simulation and experimental results from the experimental system verify the proposed damping algorithm.
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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".