Real-Time Monitoring and Control of Tobacco Weighing Systems Using Fuzzy Logic Controllers

In this study, a fuzzy logic-based real-time monitoring framework is proposed for solving the problems of nonlinearity, time-varying dynamics and external disturbances that are common in tobacco weighing systems. The system integrates a load cell, a servo motor, and a fuzzy logic controller (FLC) based on Mamdani reasoning, which realizes the dynamic regulation of the motor speed by processing key input variables such as weight deviation and its rate of change. Experimental results show that the FLC is able to quickly stabilize in $\mathbf{1. 2}$ seconds for a weight perturbation of $\mathbf{1 0 \%}$ compared to the conventional proportional-integral-derivative (PID) controller, which takes 2.6 seconds. In addition, the FLC significantly reduces steady-state errors and overshoots, and demonstrates excellent stability across a wide range of operating conditions. By combining real-time data acquisition, noise filtering and adaptive control signals, the system achieves low-latency and high-precision operation, fully verifying the superiority of fuzzy logic in addressing complex industrial challenges and laying the foundation for the development of intelligent industrial automation.