Homothetic tube MPC for the trajectory tracking of autonomous groundvehicles with guaranteed transient performance
Bibliographic record
Abstract
In the past decades, autonomous ground vehicles (AGVs) are becoming attractive in a wide domain of domestic, industrial, and agricultural applications. Among the related research on AGVs, trajectory tracking control is one of the significant and fundamental one. The main challenge for this problem is how to effectively tackle system constraints induced by the hardware and environment. Many research efforts have been devoted to addressing this issue. For the existing results in the literature, model predictive control stands out a promising solution since it can systematically and efficiently handle the constraints while guaranteeing the optimal control performance according to prescribed performance index. Therefore, the MPC-based solutions to the AGV trajectory tracking problem have been widely studied by researchers. However, when considering uncertainties, there are relatively few results since it is difficult to avoid constraint violation and ensure optimal closed-loop performance, especially for the AGVs with model uncertainties. Although the robust MPC-based solutions have been studied by researchers, most of these results are relatively conservative due to the over approximation of the effect of uncertainties when the system states evolve over time. This approximation process is usually conducted offline in the robust MPC framework, thereby increasing the conservatism of handling the parametric uncertainties. In addition, most results focus on whether the AGV can finally track the desired trajectory, The transient tracking performance is not considered, which is also of paramount importance for the practical applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".