Nonlinear Behaviors of a Half-Car Magneto- Rheological Suspension System Under Harmonic Road Excitation
Bibliographic record
Abstract
Magneto-rheological (MR) suspension systems offer meritorious potential for realizing an effective compromise between driving comfort and handling performance. The inherent hysteresis nonlinearity of a magneto-rheological damper (MRD), however, may lead to unpredictable and complex dynamic behavior of the vehicle system. In this study, a pitch-plane half-car model comprising MR suspension with hysteresis nonlinearities was formulated to investigate its dynamic responses as a function of the driving speed. The dynamic stability of the vehicle model was investigated under harmonic excitations through Lyapunov exponents to globally illustrate strong dependencies on the excitation frequency and amplitude. The influences of driving speed, and excitation amplitude and frequency on the nonlinear response characteristics were analyzed through bifurcation diagrams, phase portraits and Poincaré maps. The dynamic evolution of periodic motion to chaotic motion was illustrated through Hopf, saddle node and period-doubling bifurcations, respectively, under low-, mid- and high-speeds. Moreover, the hyperchaotic oscillation of the system was observed for the first time. The results show that the nonlinear response behavior of the half-car MR suspension is mostly concentrated in the medium frequency range for low- as well mid-speeds, which is concerned with ride comfort performance of the vehicle. The results obtained in the study provide essential basis for further investigations on effective controller synthesis and stability analyses of more practical higher order models of vehicles with MR suspension.
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 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".