Design of a Novel Fuzzy Controller to Enhance Stability of Vehicles
Bibliographic record
Abstract
This paper presents the design of a novel fuzzy control structure to improve stability of vehicles with semi-active suspension system. The proposed fuzzy controller adjusts the damping coefficient to stabilize the sprung mass and hence reduce the tendency of vehicle to rollover. A full car model with eight degrees of freedom is adopted that includes the vertical, roll, yaw, and pitch motions as well as the vertical motions of each wheel. Four decentralized fuzzy controllers are developed and applied to each individual damper in the vehicle suspension system. The controllers input(s) are lateral acceleration and vehicle states and the output is an adaptive damping coefficient. Mamdani's inference engine is used to obtain the required damping coefficient of each suspension system. To evaluate the performance of the proposed controller, experiments were performed for simple turn and lane change maneuvers. To show the effectiveness of the proposed controller, comparison is made with Cadillac controller. Results show that the fuzzy controller reduces roll angle, linear transfer ration (LTR) and hence decreases the propensity to rollover in vehicles.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".