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AntonAndreenko/pid_tuning

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App README

PID Controller Simulation and Optimization

This project is a Streamlit web application that allows for the simulation and tuning of a Proportional-Integral-Derivative (PID) controller for a First-Order-Plus-Dead-Time (FOPDT) process. Users can interactively adjust both the process and PID parameters to visualize the impact on the system's response. The application also provides an optimization feature to calculate recommended PID tuning parameters based on established methods.

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.

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.