Handling and Stability Control of Distributed Drive Electric Vehicle Based on Phase Plane
Bibliographic record
Abstract
This study investigates the coupling and interference effects between Active Front-Wheel Steering (AFS) and Direct Yaw Moment Control (DYC) in distributed drive electric vehicles. To enhance vehicle handling and stability, a coordinated control strategy for AFS and DYC is developed based on phase plane analysis. Utilizing fuzzy control theory, the phase plane is categorized into three distinct regions: a stable region, a coordinated control region, and an unstable region. To precisely compute the additional yaw moment required for stability enhancement, an adaptive sliding mode controller is designed. Furthermore, a joint sliding mode surface is formulated by considering the deviations between the actual and ideal yaw rate and sideslip angle. The weighting of the sliding mode surface is dynamically adjusted in real time using a stability index in conjunction with a cosine function. To optimally distribute the control effort between AFS and DYC, an improved simulated annealing particle swarm optimization algorithm is employed. The proposed control strategy is validated through simulations conducted on CarSim and Simulink platforms. The results demonstrate that the coordinated control system effectively enhances both vehicle handling and stability.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".