PSO-Fuzzy Gain Scheduling of PID Controllers for a Nonlinear Half-Vehicle Suspension System
Bibliographic record
Abstract
The present article addresses the gain scheduling of proportional-integral-differential (PID) controllers using fuzzy set theory coupled with a metaheuristic optimization technique to control the vehicle nonlinear suspension system. The nonlinearities of the vehicle suspension system are due to the asymmetric piecewise dampers, quadratic tire stiffness, and the cubical spring stiffness. Conventional PID controller suffers from the low performance subject to modeling nonlinearities, while fuzzy logic controller (FLC), as a universal approximator, has the capacity to deal with the nonlinear, stochastic, and complex models. However, finding the optimal Mamdani FLC rules is still a challenging task in addition to a proper architecture of the membership functions (MFs). As a remedy to this drawback, particle swarm optimization (PSO) technique is employed in this article to improve the efficiency of the FLC-based PID controllers. The proposed nonlinear controller is suggestive of the decreased overshoot and reduced settling time for the heave and pitch motions of the half-vehicle model. The satisfactory performance of the controller, when tires are subject to random excitations, to reduce the peak magnitude is observable in a relatively less computational time.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".