Racing miniature cars: Enhancing performance using Stochastic MPC and disturbance feedback
Bibliographic record
Abstract
We study and compare different Stochastic Model Predictive Control (MPC) approaches for driving miniature race cars, where the goal is to race the cars as fast as possible around a known track. Designing such controllers is generally challenging since the models are not entirely accurate due to linearization errors and model mismatch. Consequently, deterministic MPC tends to be optimistic, causing the cars to leave the race track, resulting in accidents. To mitigate these shortcomings, methods based on Stochastic MPC have been proposed that ensure constraint satisfaction with high probability. Furthermore, one can reduce conservatism of the solution by optimizing over feedback policies at the expense of increased computational time. While methods based on affine state feedback policies have been shown to perform well, their performance critically depends on the choice of the feedback matrix. One way to ensure computational tractability is to fix the feedback matrix in an a-priori computation which is typically obtained via trial-and-error. To overcome this issue, we investigate the benefits of disturbance feedback policies, which allows us to (indirectly) optimize over the state feedback matrices. We verify the benefits of disturbance feedback policies in simulations, and also implement a heuristic variant on the actual system in experiment. Both studies suggest that Stochastic MPC with disturbance feedback is an attractive alternative to existing methods, due to its ability to increase performance and robustness compared to deterministic methods.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".