Coordinated control of DBS and RWS based on the hybrid of data-driven model-free control and fuzzy allocation
Bibliographic record
Abstract
The vehicle stability is the core of the control of the intelligent chassis. Previous studies achieved vehicle stability using multi-actuators. However, they hardly considered the execution allocation of multi-actuators, leading to a balance difficulty between the handling stability and ride comfort. This paper presents a coordinated control strategy for the differential braking system (DBS) and rear wheel steering (RWS) to enhance yaw control through their optimal control and execution. The proposed strategy has a hierarchical structure with three layers. The upper layer calculates the additional yaw moment using a data-driven model-free control. The medium layer coordinates the DBS and RWS, dynamically allocating execution proportion based on fuzzy logic. The lower layer converts the allocated yaw moment into distributed braking torques and RWS angles. Vehicle tests during the slalom, steady-state circular, and double-lane change were conducted. The results demonstrated that the proposed coordinated strategy can simultaneously achieve the optimal control and execution of the DBS and RWS, generating a 32.7% improvement in yaw rate tracking accuracy and a 20.6% reduction in steering wheel angle requirements compared to existing control strategies in industry.
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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 | 0.001 | 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".