Lyapunov exponents-based stability analysis and integrated control of rollover mitigation and yaw stabilisation of ground vehicles
Bibliographic record
Abstract
Yaw stabilisation and rollover prevention are two key factors in safety of vehicles. Designing a controller that can address both of the safety concerns and evaluating the performance of such a control system using a proper stability analysis tool is of interest. The complexities in dynamics of vehicles, such as tyre dynamics, make the stability analysis of such systems more challenging. In addition, maintaining both of the above objectives, the yaw motion control and rollover mitigation, is contradictory when a vehicle experiences high lateral accelerations. In this research, a T-S fuzzy controller is proposed which prioritises the concern that must be addressed at each time instant based on the state of the vehicle. The controller compromises between the two contradictory objectives of tracking a desired value for the yaw rate and maintaining the rollover index within a limit to ensure wheel lift-off does not occur. A novel method using the concept of Lyapunov exponents is employed to perform system and structural stability analysis. Results of the stability analysis together with simulations demonstrate the advantages of the proposed controller.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".