A Universal and Reconfigurable Stability Control Methodology for Articulated Vehicles With Any Configurations
Bibliographic record
Abstract
To meet with many different transportation needs, it comes in a rich diversity and variety of articulated vehicles. Vehicle combinations are seen in different axle configurations, number of articulations, powertrain, active actuation systems, etc. This research is, therefore, motivated to develop a model-based control system in a universal and reconfigurable fashion to any articulated vehicles stability control. To achieve its universality and reconfigurability, we introduce a hierarchical (two-layer) control system. Namely, the high layer formulates a model predictive control (MPC) tracking problem to generate corrective Center of Gravity (C.G.) forces/moment. The lower-level controller is formulated as Control Allocation (CA) algorithm to regulate steering or torque (brake) at each wheel optimally and reconfigurable as to meet high-level calculations. Real-time constraints, i.e. actuator limits, tire capacity, and actuator failure are discussed. Diverse applications are presented that the universal and reconfigurable methodology is handy, capable and effective on stability control while applying to various vehicle configurations and objectives.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".