Research on Cooperative Control of the Hydraulic System of Multiple Intelligent Vehicles Combined Transportation
Bibliographic record
Abstract
The multi-vehicle combined transportation of large-scale equipment or goods is studied, and various combined transportation modes are obtained. The research on four-vehicle combined transportation is studied, the four transport vehicles must ensure synchronization in the process of running, and the steering must be coordinated, otherwise major accidents may occur. Aiming at the stability control of multi-vehicle running synchronization, the system transfer function of pump-controlled motor in driving system is established, and the PID control is added. The simulation results show that adding the PID control algorithm can improve the speed stability of the transport vehicle. And the geometric model of the steering mechanism is established, the functional relationship between the steering angle and the stroke of the steering cylinder is obtained, and the relationship between the electric signal of proportional valve and the steering angle is deduced. On this basis, the coordinated control system of four-vehicle running synchronization and steering coordination based on CAN (controller area network) bus is designed. The master-slave synchronization control strategy and the PID control are applied to the four-vehicle combined transportation. According to the data collected from the test, it is proved that the control strategy fully meets the transportation requirements, and can provide theoretical basis and design method reference for the safe and reliable combined transportation of various types of transport vehicles.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".