Advanced optimization of a two-degree-of-freedom PID controller for air pressure monitoring sensor using a multi-objective genetic algorithm

This study investigates the optimization of a 2-Degree-of-Freedom Proportional-Integral-Derivative (2DOF-PID) controller for an air pressure monitoring sensor system using a Multi-Objective Genetic Algorithm (MOGA). The research addresses the common challenge of time delays in real-world control systems, which often stem from sensor latency, actuator dynamics, and signal transmission lags which are factors that compromise system stability and performance. To address this, the system was mathematically modeled using a transfer function to represent the dynamic behavior of the air pressure monitoring sensor, a key component in regulating pneumatic systems. The 2DOF-PID controller was implemented to independently manage reference tracking and disturbance rejection, providing greater control flexibility. The MOGA was employed to fine-tune the controller parameters based on three standard performance indices: Integral of Absolute Error (IAE), Integral of Squared Error (ISE), and Integral of Time-weighted Absolute Error (ITAE). For comparison, other optimization algorithms such as ChASO, GA, MOPSO, and ISCA were also applied. Simulation results demonstrated that the MOGA-optimized controller outperformed all other approaches, achieving superior performance metrics: -82.9% flow disturbance rejection, -76.8% temperature disturbance rejection, 1.24% overshoot, no undershoot, a fast-settling time of 44.25 seconds, and a rise time of 53.2 seconds. These results highlight the MOGA’s effectiveness in enhancing the robustness and responsiveness of pneumatic control systems.