🎛️ Interactive PID Control Dashboard

A real-time, interactive GUI for simulating and tuning PID Controllers. Designed to bridge the gap between control theory math and visual intuition.

🚀 Overview

This tool allows engineers and students to visualize how Proportional ($K_p$), Integral ($K_i$), and Derivative ($K_d$) gains affect a physical system. Unlike static plots, this dashboard lets you drag sliders and see the step response update instantly.

You can simulate any linear system by defining its Transfer Function coefficients directly in the interface.

✨ Features

• Real-Time Tuning: Adjust PID gains and watch the graph react immediately.
• Custom Plant Models: Define any system transfer function $G(s)$ using numerator/denominator coefficients.
• Live Equation Preview: Instantly sees the math representation of the system you are building (e.g., $\frac{1}{s^2 + 0.5s + 2}$).
• Robust Simulation: Uses scipy.signal to handle complex differential equations behind the scenes.
• Safety Features: "Confirm" button prevents crashes while typing coefficients; error handling for unstable/invalid systems.

📦 Prerequisites

You need Python 3.x and the following scientific libraries:

pip install numpy matplotlib scipy

🛠️ How to Run

1. Clone the repository (or download the pid_tuner.py file).
2. Install dependencies using the command above.
3. Run the application:

python pid_tuner.py

🧠 The Math Behind It

The simulator solves the closed-loop transfer function $T(s)$ for a standard feedback loop:

1. The Plant $G(s)$: The physical system you defined (e.g., a motor, a heater).
2. The Controller $C(s)$: $$C(s) = K_p + \frac{K_i}{s} + K_d s$$
3. The Closed Loop: $$T(s) = \frac{C(s)G(s)}{1 + C(s)G(s)}$$

The software then computes the Step Response (the system's reaction to a sudden change in desired position) over the selected time duration.

📝 Usage Example

To simulate a "Bouncy" Mass-Spring-Damper system:

1. Numerator: 1
2. Denominator: 1 0.5 2 (Represents $1s^2 + 0.5s + 2$)
3. Tune:
   • Increase $K_p$ to reach the target faster (will overshoot).
   • Increase $K_d$ to add "friction" and stop the bouncing.
   • Increase $K_i$ to fix steady-state error (if any).

👨‍💻 Developer

Rabah Djebbes