Integration of optimal engine and driveline controllers to minimize driveline clunk and shuffle
Bibliographic record
Abstract
Automotive control algorithms have been moving from traditional, rule-based control algorithms to optimal, model-based control algorithms. While the model-based algorithms have shown to provide better control performance over a wide range of use cases with reduced calibration efforts, it is challenging to integrate multiple model-based controllers that are developed independently to address different control objectives. In this work, a nonlinear model predictive engine controller designed for torque tracking is integrated with a reference governor-based driveline controller designed to reduce vehicle drivability problems known as clunk and shuffle. The design of both controllers is discussed and their individual performance is demonstrated for real-world test conditions and realistic driving scenarios. Then, the integration between the two optimal controllers is discussed and a proof of concept use case showing the coordinated control between the two optimal controllers is presented. The results illustrate that without coordination between the two controllers neither of them are able to meet their original control objectives.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".