Decentralized Robust Control for Vehicle Platooning Subject to Uncertain Disturbances via Super-Twisting Second-Order Sliding-Mode Observer Technique
Bibliographic record
Abstract
Platoon-based vehicular cyber-physical systems (VCPSs) have attracted much attention due to their potential to improve road capacity and energy efficiency. However, the comprehensive effect of the mismatched modeling dynamics and unknown disturbances can impose a great challenge on the convergence and stability of vehicle platooning. In this paper, we propose a novel decentralized robust control approach to address the external disturbances in vehicle platooning. Specifically, by combining a super-twisting second-order sliding mode (SOSM) strategy and a disturbance observer (DO), we design a super-twisting SOSMDO platoon controller. We also derive some design conditions of the controller and observer gains. Using the Lyapunov methodology, we theoretically prove under the design conditions the finite-time convergence of the super-twisting SOSMDO to the platooning equilibrium state and its closed-loop stability to the disturbances. Extensive simulations have been conducted and the results demonstrate the superior performance of the proposed control approach in terms of inter-vehicle spacing, velocity tracking, and platoon robustness.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".