Modeling and parameter identification of a tractor semitrailer/driver closed-loop system using the Simulated Annealing (SA) optimization approach
Bibliographic record
Abstract
Identifying the dynamics and parameters for an articulated heavy vehicle/driver (tractor semitrailer/driver) system is presented in this paper. The dynamic behaviour of the vehicle system is studied by setting up a closed-loop control model of a vehicle/driver system that included a three degree of freedom (3-DOF) tractor semitrailer lateral dynamic model and a driver steering model. It is assumed that the driver steering control model responds to the vehicle state vector following a time delay. The inherent driver steering control parameters to be identified are the state vector's coefficients, here defined as an optimal control vector. The Simulated Annealing (SA) optimization method is used to search for this control vector. The results show that this method (SA) gives a good convergence rate and robustness for the closed-loop vehicle/driver system under study. Various simulations and sensitivity analysis are also performed and presented.
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 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".