Performance Evaluation of the Optimal Control of a Gantry Crane
Bibliographic record
Abstract
For a gantry crane, optimal control of the crane motion requires that the speed of the cart be maximized, and the swing of the hanging payload be minimized. The problem lends itself naturally to optimal linear quadratic (LQ) controllers. This paper examines the performance of four different approaches to the design of an LQ controller, including two optimization approaches as based on: 1) minimal energy of cart and payload and 2) integrated absolute error of payload angle. Both simulation and experimental results are presented. A demonstration is also given as to how the results taken from laboratory scale gantry crane experiments must be treated with caution. Laboratory based studies have generally worked with systems where the moving cart mass is much larger than the suspended payload mass. In the case of industrial scale gantry cranes, the reverse can be true. This has implications with respective to the robustness of the controller. In the case where the cart mass is much greater than the payload mass, the effect of the payload on the cart is correctly neglected. However, there are stability implications if this is not the case. The implications with respect to the tuning of LQ controllers for this application are discussed.
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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 | 0.000 | 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) | 0.000 | 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".