Design of a Fuzzy Controller for Independent Control of Front Wheels Steering Angles
Bibliographic record
Abstract
In this study a novel control strategy for independent control of front wheels’ steering angles, using fuzzy logic control, has been developed. For this purpose at first the appropriate vehicle dynamic models have been introduced. A simplified two-degree-of-freedom model is considered as the reference model and then a comprehensive eight-degree-of-freedom model is developed as the simulation tool. In the next step, the comprehensive control system has been designed. The control system based on the yaw rate error of the actual vehicle comparing to the predefined reference model, the actual vehicle side slip angle and also lateral acceleration of the actual vehicle, calculates the correction steering angles of the front inner and outer wheels. Finally, a sophisticated precise numerical simulation is performed. In order to ponder the performance of the proposed controller, an optimal control system as an active steering control (ASC) has been used for comparison. The simulation results show considerable improvement in handling and stability of the vehicle compared to the conventional non-controlled system and also a vehicle equipped with an optimal controller ASC.
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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) | 0.001 | 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 | 0.000 | 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".