Development of an Adaptive and Weighted Model Predictive Control Algorithm for Autonomous Driving With Disturbance Estimation and Grey Prediction
Bibliographic record
Abstract
This paper presents an adaptive and weighted model predictive control (MPC) algorithm for autonomous driving with disturbance estimation and prediction. Unexpected and unpredictable disturbances in the real world limit the performance of MPC. To overcome this limitation, this paper proposes adaptive and weighted prediction methods with a sliding mode observer and a weighting function with the grey prediction model. The sliding mode observer is designed for disturbance estimation with finite stability conditions, and the estimated disturbance is predicted using the grey prediction model. Based on the adaptive and weighted prediction method, the length of prediction horizon and cost value of each predicted state are adjusted in real time to eliminate any negative impact on future predicted states. Meanwhile, a variation in the cost value, which is caused by prediction horizon adaptation and weighted prediction, may harm the control performance as it can excessively increase or decrease the model uncertainty. Therefore, an input weighting factor is adapted in the MPC cost function based on an exponential weighting function. The performance of the proposed adaptive control algorithm is evaluated using CarMaker software under longitudinal and lateral autonomous driving scenarios.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".