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.Recently, a novel robust MPC approach, named homothetic tube nonlinear MPC, is reported in the literature.Compared with the robust MPC approaches, homothetic tube MPC enables the online approximation of effect of uncertainties on predicted states by considering the tube cross-section parameters as the decision variables for the MPC optimization problems, thereby leading to the enhanced control performance with comparable computational complexity to the standard tube MPC methods.Motivated by this, in this work, we aim to develop the homothetic tube nonlinear MPC algorithm for the trajectory tracking of AGVs subject to both parametric uncertainties, external disturbances, and actuator saturation.An additional state constraint will be constructed and imposed to the MPC optimization problem to guarantee the transient tracking performance.Then a novel tube propagation strategy will be developed for the robust satisfaction of the input and transient performance constraints.Finally, a numerical example and comparison study will be provided to evaluate the performance of the proposed approach.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".