A Novel Fuzzy Controller to Improve Desired Suspension Performance
Bibliographic record
Abstract
Semi-active suspension systems allow for adjusting the vehicle shock damping and hence improved suspension performance can be achieved over passive methods. This paper presents the design of a novel fuzzy control structure to concurrently improve ride comfort and road handling of vehicles with semi-active suspension system. A full car model with seven degrees of freedom is adopted that includes the vertical, roll, 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. Mamdani’s method is applied to infer the damping coefficient output from the fuzzy controller. To evaluate the performance of the proposed controller, numerical analyses were carried out on a real road bump. Moreover, results were compared with well-known and widely used controllers such as Skyhook. It is shown that the proposed fuzzy controller is capable of achieving enhanced ride comfort and road handling over other widely used control methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".