Formation Control of Multiple Non-Holonomic Mobile Robots Using a Generalized PID Controller
Bibliographic record
Abstract
This study addresses the challenge of formation control for multiple non-holonomic unicycle mobile robots, a critical aspect of energy-efficient multi-agent robotic systems.A novel Generalized Proportional-Integral-Derivative (GPID) controller is proposed, grounded in Generalized Proportional Integral control theory, to ensure robust trajectory tracking and formation maintenance under uncertainties and disturbances.A dynamic model of unicycle robots is derived, and the GPID controller is designed to regulate cooperative formations.Stability is rigorously established using Lyapunov theory.Extensive MATLAB simulations demonstrate the controller's superior performance, achieving enhanced stability, reduced tracking errors, and robust disturbance rejection compared to conventional PID controllers.Evaluated formation patterns confirm the approach's adaptability in dynamic, energy-constrained environments.The results validate precise trajectory tracking and reliable formation control, underscoring the GPID controller's potential in advancing robust and scalable strategies for cooperative robotics.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".