Yaw Stability Enhancement of Articulated Commercial Vehicles via Gain-Scheduling Optimal Control Approach
Bibliographic record
Abstract
In this paper, a gain-scheduling optimal control approach is proposed to enhance yaw stability of articulated commercial vehicles through active braking of the proper wheel(s). For this purpose, an optimal feedback control is used to design a family of yaw moment controllers considering a broad range of vehicle velocities. The yaw moment controller is designed such that the instantaneous tractor yaw rate and articulation angle responses are forced to track the target values at each specific vehicle velocity. A gain scheduling mechanism is subsequently constructed via interpolations among the controllers. Furthermore, yaw moments derived from the proposed controller are realized by braking torque distribution among the appropriate wheels. The effectiveness of the proposed yaw stability control scheme is evaluated through software-in-the-loop (SIL) co-simulations involving Matlab/Simulink and TruckSim under lane change maneuvers. Simulation results demonstrate that the proposed gain scheduling optimal controllers can yield enhanced yaw stability of articulated commercial vehicles by the updating of control gains with the change of vehicle velocity.
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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 | 0.000 | 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) | 0.001 | 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".