Control Performance Analysis of Mining Ship Heave Compensation System Based on Fuzzy Logic Algorithm
Bibliographic record
Abstract
With the depletion of land resources, demands for marine resources mining is increasing, the mining vessels which is known as one of the most important offshore equipment are widely concerned. The motions of wave surges in the marine environment will affect the operation of mining vessels, especially for the heave motion which will cause serious disturbances to the mining vessels during operation, and even the damage of the hull structure and related mining facilities. Hence, it is very necessary to compensate for the heave motion of the mining vessel, and a hydraulic cylinder-based active-passive integrated composite heave compensation system is designed in this paper. Firstly, the structural composition and working principle of the heave compensation system are introduced, and its mathematical model is also established. Secondly, the frequency domain and time domain analysis are carried out using AQWA software to obtain the heave displacement response of the ship under regular and irregular waves of six sea states as the desired compensation displacement. Finally, the controller of the active-passive heave compensation system is designed, and a fuzzy logic control strategy based on Mamdani type is proposed, and the control performance is analyzed. The results show that the accuracy of the fuzzy logic control algorithm is better than the traditional PID control algorithm, and the laws and methods of PID adjustment are obtained, which proves that the fuzzy logic control algorithm has obvious advantages for engineering applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".