Features

• Interactive Simulation: Visualize the real-time response of a PID-controlled system by adjusting parameters in the sidebar and viewing the SP and PV chart.
• Customizable Process Parameters: Adjust the Process Gain (Kp), Time Constant (tau), and Dead Time (theta) of the FOPDT model.
• Real-time PID Tuning: Modify the Proportional (Kp), Integral (Ki), and Derivative (Kd) gains of the PID controller and observe the effects on the simulation.
• Performance Metrics: The simulation calculates and displays key performance indicators, including Integral Absolute Error (IAE), Control Output Index (COI), and an Oscillation Index.
• Automated PID Tuning: On-demand calculation of optimal PID parameters using well-known tuning rules:
  • Ziegler-Nichols
  • Cohen-Coon
  • IMC (Lambda)
• System Identification: The application can estimate the process model parameters from the simulation data using a differential_evolution optimization algorithm.

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Code Overview

• app.py: This is the main file that creates the Streamlit user interface. It handles user inputs, runs the simulation, displays the output chart, and manages the optimization process.
• simulation.py: This file contains the core logic for the simulation. The ProcessModel class represents the FOPDT process, the PIDController class implements the control algorithm, and run_simulation executes the simulation loop.
• optimization.py: This file includes the functions for identifying the process model and calculating optimal PID tuning parameters. It uses differential_evolution to find the best-fit model and then calculates tuning parameters for Ziegler-Nichols, Cohen-Coon, and IMC methods.