Non-Linear Bifurcation Stability Analysis for Articulated Vehicles with Active Trailer Differential Braking Systems
Bibliographic record
Abstract
This paper presents nonlinear bifurcation stability analysis of articulated vehicles with active trailer differential braking (ATDB) systems. ATDB systems have been proposed to improve stability of articulated vehicle systems to prevent unstable motion modes, e.g., jack-knifing, trailer sway and rollover. Generally, behaviors of a nonlinear dynamic system may change with varying parameters; a stable equilibrium can become unstable and a periodic oscillation may occur or a new equilibrium may appear making the previous equilibrium unstable once the parameters vary. The value of a parameter, at which these changes occur, is known as “bifurcation value” and the parameter is known as the “bifurcation parameter”. Conventionally, nonlinear bifurcation analysis approach is applied to examine the nonlinear dynamic characteristics of single-unit vehicles, e.g., cars, trucks, etc. Little attention has been paid to investigate the feasibility and effectiveness of the bifurcation analysis method for nonlinear stability analysis of articulated vehicles under varied operating conditions, e.g., varied forward speed and trailer payload. This motivates the research to examine stability boundaries of equilibrium and limit cycles in the parameter space and to predict qualitative changes in system’s behaviour (bifurcations) occurring at their equilibrium points. To this end, a nonlinear yaw-roll model with 6 degrees of freedom (DOF) is generated to simulate the nonlinear dynamics of articulated vehicles. The nonlinear yaw-roll model is also used to design an ATDB controller based on a fuzzy logic control technique. The bifurcation analysis based on the phase-plane method is conducted to evaluate the yaw and roll stability of the articulated vehicle. Built upon the conventional bifurcation analysis for single-unit vehicles, an innovative bifurcation analysis technique is developed in order to effectively assess the nonlinear stability of articulated vehicles. The applicability and effectiveness of the newly developed technique is examined and demonstrated.
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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 | 0.001 | 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".