Motion Control of a Differential Drive Mobile Robot Considering Voltage and Current Limits
Bibliographic record
Abstract
The mobile robot trajectory tracking problem, whereby a controller is responsible for ensuring a robot follows a predetermined trajectory is investigated in this work. Several different algorithms are implemented as the controller for a differential drive wheeled robot in this study, and their performances are examined across different operating conditions using several performance measures. Specifically, we implement proportional, integral, and derivative controls, as well as sliding-mode control and model predictive control, and observe their control performance in ideal, tuned operating conditions, as well as in the face of varying levels of sensor noise, actuator saturation due to voltage and current constraints, or wheel slippage in one or both wheels. Background on the kinematic model of the differential drive wheeled robot as well as the implementation and tuning of the controllers are included in this work. Furthermore, we present a discussion of the advantages and limitations of each controller in the face of varying circumstances for the task of controlling a differential drive wheeled robot.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".