Disturbance Observer-Smith Predictor Compensation-Based Platoon Control with Estimation Deviation
Bibliographic record
Abstract
In the research of cooperative adaptive cruise control, the coupling relationship between the communication delay, the following time headway, and the string stability leads to a stringent limit on the minimum allowable following time headway. To deal with this limitation, this paper proposes the Disturbance Observer-Smith Predictor Compensation (DOB-SPC)-based Master-Slave architecture. The SPC is adopted to move the communication delays and actuator delays outside the feedback loop in the Master-Slave architecture. It theoretically realizes decoupling when parameters are estimated accurately and achieves zero-headway-string-stability for arbitrary values of communication delay. Subsequently, the robust performance of SPC with estimation deviation of vehicle model dynamic lag, actuator delay, and communication delay in practical application is discussed through the changing trend of the minimum string-stable time headway. To alleviate this problem, the DOB is designed to compensate for the interference caused by the perturbation of vehicle dynamic parameters and improve the estimation accuracy. The simulation results of a four-vehicular platoon show that the string stability and following accuracy are fully guaranteed by applying the proposed strategy, even with smaller headway in the presence of estimation deviation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".