TRAJECTORY TRACKING OF NONHOLONOMIC CONSTRAINT MOBILE ROBOT BASED ON ADRC, 1-9. SI
Bibliographic record
Abstract
The wheeled mobile robot is a typical uncertain robot system, which has typical nonholonomic characteristics and underdrive characteristics with different input and output dimensions.In the unknown environment, the slip and sideslip of the wheeled mobile robot are inevitable.Therefore, the research on trajectory tracking control of the wheeled mobile robot under sliding is of great significance.Aiming at the tracking problem of the wheeled mobile robot system with side slip disturbance, a Lyapunov design method based on extended state observer is proposed by using active disturbance rejection strategy.The influence of the sideslip disturbance on the system is regarded as the total disturbance of the system, and the extended state observer is used to observe the total disturbance in real time.The observation of the total disturbance by the extended state observer plays a patch role in the control law.When there is no disturbance, the system returns to the Lyapunov control under the nominal model.When there is a disturbance, the disturbance is compensated by the extended state observer, so that the system is approximately equivalent to the nominal model without being affected by the disturbance.The simulation results show that the system has better transient performance and anti-interference performance compared with the inverse control regardless of whether it is disturbed or not.
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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 | 0.000 |
| 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) | 0.003 | 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".