Optimization of the Backstepping Control Parameters of an Active Electrohydraulic Suspension to Improve Passenger Comfort and Road Handling
Bibliographic record
Abstract
This study introduces an innovative optimization strategy for Electro-Hydraulic Active Suspension Systems (EHASS), combining game theory with Particle Swarm Optimization (PSO) to tune backstepping control parameters. Unlike conventional approaches relying on manual tuning or trial-and-error, our method systematically optimizes these parameters, ensuring a well-balanced trade-off between ride comfort and road handling. The optimization process considers worst-case road disturbances, leading to a 79.5% reduction in tracking error, a 44.7% decrease in VDV, and a 51.2% improvement in Crest Factor, complying with ISO 2631 standards. Comprehensive validation across ten road profiles, including highly irregular terrains, confirms the robustness of the proposed method. Additionally, a comparison with Genetic Algorithm (GA)-based optimization highlights that PSO achieves superior convergence and performance. These findings establish a new benchmark for intelligent suspension control, making our approach a strong candidate for real-world automotive applications.
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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 | 0.001 | 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".