Optimisation of PID Controllers in Active Suspension Systems: A Comparative Study of the Firefly Algorithm and the Particle Swarm Optimisation
Bibliographic record
Abstract
Suspension systems are crucial for enhancing passenger comfort, steering stability, and overall ride quality.They should also ensure effective directional control during handling manoeuvres and adeptly insulate passengers from external disturbances.In this study, a comparative evaluation was conducted between the Firefly Algorithm (FA) and Particle Swarm Optimisation (PSO) for optimising proportional-integral-derivative (PID) controllers in active suspension systems.Both algorithms, inspired by natural phenomena, have been previously successful in addressing diverse problems.By employing a mathematical model of the active suspension system and the MATLAB Simulation Toolbox, the behaviour of the system under these two optimisation techniques was investigated.The primary objective was to minimise the acceleration of the sprung mass in response to varied driving conditions.Results from the simulation process suggested a notable superiority of the FA over PSO when integrated with the PID controller, particularly in reducing the acceleration of sprung masses.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".