Adaptive Control of Autonomous Mobile Robots Using Fuzzy Logic Based PID Optimization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Autonomous mobile robots require precise navigation and stability in dynamic environments, where traditional control methods often fail to balance accuracy, responsiveness, and robustness. This study proposes an adaptive fuzzy–PID control framework to optimize real-time trajectory tracking and disturbance rejection. The approach integrates a fuzzy inference system with adaptive proportional integral–derivative (PID) gain tuning, enabling continuous adjustment of control parameters based on instantaneous tracking error and error rate. The methodology combines MATLAB/Simulink and ROS Gazebo simulations with physical experiments on a differential-drive mobile robot equipped with LiDAR, inertial sensors, and high-resolution wheel encoders. Results demonstrate that the adaptive fuzzy–PID controller reduced overshoot by 42%, shortened settling time by 35%, and maintained a steady-state lateral error below 1 cm and heading deviation under 0.5°, outperforming classical PID and conventional fuzzy-PID schemes. These findings confirm robust adaptation to nonlinear dynamics and unexpected disturbances without significant computational overhead. The proposed framework emphasizes interpretability and practical applicability, providing insights for multi-robot coordination, self-driving vehicles, and industrial or service robotics where reliability and safety are critical.
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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 | 0.001 | 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 it