Application of Lexicographic Optimization Method to Integrated Vehicle Control Systems
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Bibliographic record
Abstract
In this paper, a general control allocation (CA) algorithm is proposed based on a lexicographic optimization (LO) strategy for a vehicle's longitudinal and lateral control. The primary objective of this CA is to distribute the torque adjustments of the lateral controller such that the error between the desired and actual forces and moments at the vehicle's center of gravity is minimized, while maintaining the longitudinal tire slip ratios close to a desired range. Presence of various constraints in practical systems can significantly increase the complexity and effort of properly adjusting the tuning parameters in the objective function. Hence, an LO method is used to prioritize the objectives. Lateral stability of the vehicle has the highest priority and is subject to the constraint of maintaining small tire slip ratios. The second priority of CA is assigned to minimize the adjustment torques. The proposed LO-based approach reduces exhaustive vehicle testing commonly required for tuning of the separately designed controllers and the cost and time of the implementation. In addition, it makes the algorithm easily transferable from one vehicle to another by reducing the number of tuning parameters. Simulation and experimental results are presented to show the effectiveness of the proposed approach.
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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 | 0.001 |
| 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 it