Design of an Active Suspension System using PD Controller
Bibliographic record
Abstract
Since the beginning of the transportation era, whether it was passenger or commercial vehicles, comfort of the passengers and stability of the vehicle have always been a crucial concern.With the evolution of modern vehicles, suspension systems that are responsible for providing passengers with comfortability and stability also evolved.Initially, they were purely mechanical, depending solely on the working principles of springs and dampers.However, the advancement of automotive technology has led to the introduction of more advanced systems like the Active Suspension Systems (ASS).In this study, a model-based design and tuning of an ASS was conducted.The main goal of the study was to achieve the minimum pitch angle, vertical acceleration, and vertical displacement of the car's body for a bump passing manoeuvre.A Half Car Model (HCM) was developed using MATLAB/Simulink software and validated using literature data.Using MATLAB mobile software installed at an iPhone brand mobile phone, a 2020 model year Ford F-150 vehicle was tested considering a bump passing manoeuvre, and the suspension system parameters of the vehicle were determined via matching the vehicle response with the simulation results.A Proportional Derivative (PD) controller was implemented in the model, and its parameters were tuned.A 93.54% improvement in terms of maximum pitch angle, a 96.69% improvement in maximum vertical acceleration, and a 25.17% improvement in the maximum vertical displacement of the vehicle chassis were achieved as a result of this study.To standardize the achievement of the study, a second bump profile that is based on a standard was also implemented in the model.A 94.07% improvement in terms of maximum pitch angle, a 95.99% improvement in maximum vertical acceleration, and a 24.51% improvement in the maximum vertical displacement of the vehicle chassis were achieved as a result the standardized bump passing manoeuvre.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".