Investigation of trajectory tracking control algorithms for autonomous mobile platforms: theory and simulation
Bibliographic record
Abstract
This paper considers the problem of trajectory tracking control design for autonomous mobile platforms. A solution to the problem of controlling these underactuated autonomous vehicles is proposed based on way point guidance approach combined with model reference trajectory control method. Our proposed tracking controller basically can be decomposed into two parts: i) a geometry task, which uses the model reference of converging the autonomous vehicles to the circle of acceptance of the way point and ii) a dynamics assignment task, where the way point is assigned to the reference path with a speed profile that move on the desired trajectory. At the same time the way point has its own dynamics for describing the motion, which is also associated with differential equation. We then demonstrate how way point guidance approach can be combined with model reference control law to provide the control objective to the problem of trajectory tracking. Our proposed controller is aimed to provide a solution to the position tracking problem for a fairly general class of underactuated autonomous vehicles that is applicable to motion in two and three dimensional spaces. Finally the proposed control algorithm is validated through computer simulations. This paper concludes with various simulation results and suggestions for further research.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".