Path Following Control for Four-wheel Drive Electric Intelligent Vehicle Based on Coordination between Steering and Direct Yaw Moment System
Why this work is in the frame
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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。
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it