An <scp>LMIs</scp>‐based back‐stepping sliding mode control framework for robust velocity tracking of the automatic driving vehicle on sloped roads
Bibliographic record
Abstract
Abstract Automatic driving has received a broad of attention from academia and industry since it is effective in greatly reducing the severity of potential traffic accidents and achieving the ultimate automobile safety and comfort. This article presents a back‐stepping sliding mode controller (BSMC) through linear matrix inequalities (LMIs) for the highly automatic driving vehicle on sloped roads. It includes three key modules, namely, an extended Kalman filter (EKF)‐based road slope estimation module, a robust BSMC‐LMIs velocity‐tracking controller based on the input–output feedback linearization, as well as a longitudinal inverse vehicle dynamics module. The nonlinear combined slip tire model with the transient behavior is introduced to calculate the tire forces properly, which would be further proven to offer more accurate road slope estimations even in a fierce acceleration or deceleration situation. The proposed BSMC‐LMIs controller for velocity tracking can handle the lumped uncertainties which include the modeling error, the parameter perturbation, external disturbances, and noises, and guarantee the reachability of the sliding surface, meanwhile, alleviating the chattering phenomenon inherited from the sliding mode structure. Besides, a sufficient condition for the existence of the proposed BSMC is derived by using the LMIs, which ensures the asymptotical stability on the sliding surface. Finally, the robustness, feasibility, and effectiveness of the proposed BSMC‐LMIs controller for velocity‐tracking are verified by simulation tests in various working scenarios, which shows satisfying results when dealing with the lumped uncertainties on sloped roads. Moreover, the comparative study also shows that the proposed BSMC‐LMIs controller has the best tracking performance when compared to the model predictive control, conventional sliding mode control, and the cubic proportional‐integral controller.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".