Model predictive control–based approach for assist wheel control of a multi-axle crane to improve steering efficiency and dynamic stability
Bibliographic record
Abstract
This research deals with assist wheel control to improve the steering efficiency and dynamic stability of the multi-axle crane based on model predictive control. Since multi-axle crane has relatively high inertia and long distance between the axles, it has slow dynamic response, and thus, different steering strategies according to driving speed intervals are required. Specifically, the steering strategy is that the number of wheels fixed mechanically increases to secure dynamic stability as the driving speed increases. However, although this strategy enables to secure stability by slowing down the dynamic response, it also has a weakness to decrease the steering efficiency. If the steering efficiency is decreased, it may result in augmenting an accident rate due to an increase in driver’s fatigue. Therefore, this study suggests a new steering strategy to improve the steering efficiency by simultaneously guaranteeing dynamic stability. The suggested steering control algorithm can enhance both steering efficiency and stability by deriving an assist wheel control input based on model predictive control using a variable weighting factor derived from the driver’s steering input (steering angle on the first axle). Development and performance evaluation of the suggested algorithm were conducted in the MATLAB/Simulink environment, and evaluation results confirmed that the developed algorithm could both enhance the driver’s steering efficiency and secure dynamic stability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".