Enhanced extended state observer based prescribed time tracking control of wheeled mobile robot with slipping and skidding
Bibliographic record
Abstract
Abstract This article focuses on the trajectory tracking control for a perturbed wheeled mobile robot (WMR) with slipping and skidding. The external disturbances and uncertainties caused by the slipping and skidding compose the “total” disturbance. By analyzing the stable state of the controlled system, the WMR reference model driven by a favorable disturbance is constructed. This article proposes an enhanced extended state observer (EESO) to reckon the difference between the “total” disturbance and the favorable disturbance. Then, a practical prescribed time tracking method is developed. By introducing a time‐dependent scaling function, the initial value restriction can be relaxed, and the peaking phenomenon caused by the EESO can be tolerated. With the proposed EESO and the prescribed time tracking controller, the estimation error is input‐to‐state stable, and the tracking control of WMR with global prescribed performance is achieved. Simulation results show the advantages.
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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) | 0.000 | 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 | 0.000 |
| 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".