Data Modeling and Fuzzy PID Control of Air Pressure Control System

A fuzzy PID controller design method based on a data model is proposed to address the performance degradation of traditional PID control in nonlinear systems and environments with parameter uncertainty. Initially, by examining the system's dynamic characteristics and experimental data, an appropriate data model is formulated through parameter identification, establishing a foundation for controller design. Secondly, utilizing the output error, error change rate, and control output from the data model, fuzzy logic rules are established, transforming the nonlinear relationship between the control strategy and system response into a fuzzy rule set, thus enhancing the parameter adjustment of the PID controller. The fuzzy PID controller can dynamically modify the proportional, integral, and derivative gain coefficients based on the real-time system state, thus enhancing control precision. The control algorithm is implemented in the host computer, enabling the solenoid valve to be regulated by a PWM signal, thereby validating the precise air pressure control technology on the hardware platform. The findings indicate that, in contrast to conventional PID control, the data model-based fuzzy PID controller can maintain the steady-state error within 7/100,000, exhibits superior stability, and effectively addresses system nonlinearity and external disturbances. This research offers a solution for adaptive control design in high-precision industrial process management.