Autonomous Overhead Crane System Using a Fuzzy Logic Controller
Bibliographic record
Abstract
This paper pertains to advanced automation of the load transfer process using overhead cranes. Overhead cranes are widely used in various areas of industry, including manufacturing, construction, shipping, etc. Load transfer operations using overhead cranes have to be performed fast and safely. As such, these operations are handled by expert operators however, the demand for an automatic consistent and reliable crane operation is on the rise. The crane–load system is highly nonlinear and time-varying, hence, solutions considering model-base approaches may lead to a complicated controller structure. Such a controller may require accurate estimation of the crane system parameters. In this paper we present a new fuzzy logic controller for overhead crane operation. The fuzzy controller is designed based on knowledge of an expert crane operator, and does not require any parameter estimation. It mimics the operator behavior by using the same crane–load system states that are realized by the operator. These states are the trolley position error and the load sway angle. The fuzzy controller action, on the other hand, is the desired trolley speed. The proposed controller is implemented and tested on a small-scale overhead crane. Experimental results show robust operation of the fuzzy controller as compared with that of a conventional controller.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".