Adaptive Control of Autonomous Mobile Robots Using Fuzzy Logic Based PID Optimization
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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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".