Integrated System Optimization for Enhanced Performance of a Physical Model: A MATLAB and Python Approach

This paper focuses on the development and optimization of a temperature control system utilizing a Proportional Integral-Derivative (PID) controller, with integration of MATLAB and Python. The work presented in this paper is structured around three key stages. The first stage involves the implementation of PID controllers using MATLAB’s toolbox tuning capabilities to establish a robust foundation for the temperature control system. In the second stage, Fuzzy Inference Systems (FIS) are employed to fine-tune the PID parame-ters-proportional ($K_{p}$), integral ($K_{i}$), and derivative ($K_{d}$)-to provide adaptability to varying system conditions, with the goal of enhancing system responsiveness and stability. The third and final stage introduces an innovative approach by integrating Python code into MATLAB Simulink for the design of a Fuzzy Logic Controller (FLC). Initially, the MATLAB PID Tuner App is used to automatically tune the PID parameters, establishing a basic system framework. In the second stage, the FLC is incorporated with the PID controller to improve key performance metrics such as overshoot, settling time, and rise time. The third stage further refines this by replacing the FLC with a MATLAB Function Block in Simulink, allowing for seamless integration of Python scripts. This integration enables the application of advanced algorithms and computations, thereby expanding the control system’s capabilities and facilitating the use of more sophisticated control strategies and analysis techniques.