Optimal control allocation for coordinated suspension control
Why this work is in the frame
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Bibliographic record
Abstract
This paper is concerned with applying techniques from L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -optimal control allocation for the coordinated stabilization and control of vertical vehicle dynamics (roll, pitch, and vertical motion) using active and semi-active suspensions. This is accomplished by designing appropriate high-level controllers and control allocators for multiobjective control. The equations of motion for the vertical vehicle dynamics are presented and high-level controllers are designed using techniques from sliding mode control. Optimal control allocators are then designed by way of quadratic programs with special attention given to the differences inherent in active and semi-active (MR damper) suspension systems. Finally, the control system is implemented using a computationally efficient active set algorithm and validated in simulation with a CarSim model of a Chevrolet Equinox. Simulation shows that the control system designed here substantially improves the vertical vehicle dynamic response in the case of both active and semi-active actuators.
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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 | 0.000 |
| 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 it