Application of Fuzzy PID Algorithm in Path Control of Intelligent Tracking Vehicles

This paper addresses the path control problem of an intelligent self-tracking car in dynamic environments and proposes a control algorithm based on fuzzy PID. The algorithm controls the car’s pose by adjusting the speed difference of the driving wheels, combining the high adaptability of fuzzy logic with the efficiency of PID control, effectively handling the uncertainty of input signals. The algorithm detects position and angle deviations in real time, comprehensively adjusts the control strategy, and precisely regulates the driving wheel speed, thereby quickly and accurately correcting the car’s travel direction and position, significantly reducing errors. By constructing a kinematic model of the AGV and using Simulink for simulation, the results show that fuzzy PID performs excellently in tracking step signals. When there is an offset input, the fuzzy PID controller makes the AGV converge to the predetermined trajectory more quickly, with smaller overshoot, less fluctuation, smoother changes, and stronger system stability. Additionally, this paper deeply compares the performance of the two algorithms under various experimental path conditions. The experimental results show that the fuzzy PID algorithm outperforms the traditional PID in various experimental environments, with smaller variance, especially in handling complex path tracking tasks. It achieves lower curve error, more accurately controls the car’s state, and significantly enhances navigation performance.