Path Following Control for Four-wheel Drive Electric Intelligent Vehicle Based on Coordination between Steering and Direct Yaw Moment System
Bibliographic record
Abstract
摘要： 为提高智能汽车的路径跟踪能力，并保证其在极限工况下的动力学稳定性，以四轮驱动智能电动汽车为研究对象，根据转向和主动横摆力矩(Direct yaw moment，DYC)系统的特点分别设计控制律进行协调控制。首先，针对汽车在转向过程中轮胎侧偏刚度的不确定性，利用线性矩阵不等式(Linear matrix inequality，LMI)理论构造可实现系统区域极点配置的鲁棒控制器，并研究其求解方案。然后，采用分层架构设计主动横摆力矩的控制律；其中，上层控制器通过车-路运动学关系，基于线性时变模型预测控制(Linear-time-varying model predictive control，LTV-MPC)计算期望横摆角速度；下层采用基于双曲正切趋近函数的滑模控制计算主动横摆力矩，为了在提高跟踪精度的同时确保汽车动力学稳定性，在滑模面中引入质心侧偏角的控制权重，其大小根据质心侧偏角稳定性相图确定。考虑到在大多数常见工况中，转向系统单独作用就已经可以取得良好的控制效果，对主动横摆力矩系统设置激活机制，使其仅在转向系统被判定难以完成当前控制目标时才介入，避免了正常工况下的非必要激活引起的耗能。最后，通过Simulink-CarSim联合仿真进行了算法验证，结果表明，即使在较极端的工况下，所提出的控制方法仍然能保持良好的循迹控制效果，并且可以很好地确保汽车的动力学稳定性。
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".