Controller Design of Quarter Car Suspension System and Stability Analysis of the Linear System
Bibliographic record
Abstract
The suspension system plays a pivotal role in ensuring the safety, stability, and comfort of passengers in a vehicle by absorbing shocks and vibrations caused by road irregularities. The primary purpose of the suspension system is to improve passenger comfort and facilitate safe handling by maintaining tire contact with the road surface. This paper proposes a linear model of a quarter-car suspension system, focusing on its dynamic behavior under typical driving conditions. The suspension system operates to counteract disturbances from road irregularities, such as bumps, potholes, and uneven surfaces, which can impact vehicle stability. Though the system’s dynamics are generally nonlinear due to the complexity of real-world road conditions and mechanical components, this study simplifies the model to a linear approximation. Controller design comparison using a traditional PID controller and a State Feedback Controller is done for the system, and the result from MATLAB can be used for further analysis. Lyapunov stability analysis is employed in this paper to evaluate the system’s stability, ensuring that the suspension system remains within a stable operating range and does not lead to uncontrolled oscillations or instability. The linear model, though an approximation, allows for identifying critical parameters that affect system performance and the development of design strategies that enhance vehicle safety and ride quality.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".