Position Control and Anti-Sway of Overhead Crane System with Uncertain Nonlinear Model
Bibliographic record
Abstract
Overhead cranes are used to move heavy, bulky objects above the factory floor instead of along floor walkways.They are commonly used to load and unload goods in factories, outdoor warehouses and serve at stations, ports.During operation, chain hoists or cable hoists are the main equipment, plays the role of hoisting/lowering materials and moving mechanism along the main beam.Therefore, vibration cannot be avoided during the process of moving heavy objects, causing danger to people and affecting the product.In addition, the overhead crane is an uncertain nonlinear system, compared to the single pendulum type, moving two loads at the same time is much more complicated.That's why the author proposes to design a new controller that not only helps balance the cart but also the two pendulums during operation.First, the system dynamics model is built.Next, an adaptive controller based on the radial basis function neural network (RBFNN) is designed and proven to be stable according to Lyapunov theory.Simulation results of the overhead crane system on MATLAB/Simulink software have shown the effectiveness of the proposed algorithm even when the working system is affected by model uncertainty.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".