Enhanced Ball Trajectory Tracking Using Visual Servoing with 2-DOF Ball on Plate Balancing System
Bibliographic record
Abstract
Accurate tracking of ball trajectories on a platform using a 2-DOF balancer system poses significant challenges in the existing literature due to its inherent nonlinearity and instability.This research addresses these challenges through a two-fold approach.Firstly, we focus on designing a robust 2-DOF ball balancer system model.Secondly, we perform a comparative study of three control techniques: Linear Quadratic Regulator-based Proportional (LQR_P), Full State Feedback-based Proportional (FSF_P), and Classical Proportional Derivative-based Proportional (PD_P) control.To evaluate the effectiveness of the designed controllers, we conduct both simulation and experimental tests using MATLAB Simulink integrated with Quarc software and the 2DOF ball balancer system Quanser hardware.The results demonstrate that the Linear Quadratic Regulator-based proportional control exhibits superior performance in terms of transient response, including percentage overshoot, settling time, and peak time.Moreover, it showcases excellent steady-state response, achieving a minimum steady-state error of 0.641mm, outperforming the other techniques investigated in this study.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".