Adaptive Control of Autonomous Mobile Robots Using Fuzzy Logic Based PID Optimization

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.