PID.code

This repository contains a unified MATLAB and Python implementation of a reusable, feature-complete PID controller, together with a collection of simulation examples that reproduce common industrial and academic control scenarios.

The project is intended as a teaching, experimentation, and reference codebase for practical PID implementation, covering both fundamental and advanced control strategies.

---

📦 Repository Structure

.
├── matlab/        % MATLAB implementation and examples
├── python/        % Python implementation and examples
└── README.md      % This file

Each language folder is self-contained and includes:

• A reusable PID controller implementation
• Simulation scripts for different control scenarios
• Shared utilities for process definition and plotting

---

🎯 Project Goals

This repository is not a packaged library or application. Instead, it focuses on:

• Providing a clear and reusable PID implementation
• Demonstrating practical control engineering techniques
• Allowing side-by-side comparison between MATLAB and Python implementations
• Serving as a didactic resource for students and practitioners

---

⚙️ Controller Features

Both implementations (MATLAB and Python) support:

• P, PI, PD, and PID control
• Manual, automatic, and tracking modes
• Setpoint weighting (b, c)
• Feedforward integration
• Output saturation and rate limiting
• Anti-windup via back-calculation
• Second-order measurement filtering
• Controller duplication (copy/clone functionality)

---

🧪 Example Scenarios

The included examples cover typical control engineering situations:

• Manual to automatic transfer (bumpless switching)
• P / PD operation via integral deactivation
• Setpoint weighting effects
• Feedforward compensation
• Anti-windup strategies
• Measurement noise filtering
• Gain scheduling (linear and nonlinear systems)
• Selector / override control

Each example follows a consistent structure:

1. Load process and simulation data
2. Configure controller(s)
3. Run a discrete-time simulation loop
4. Store signals
5. Plot results

---

🔁 MATLAB vs Python

Both implementations are conceptually equivalent and designed to mirror each other:

Aspect	MATLAB	Python
Controller	@PID/PID.m class	PID.py class
Simulation scripts	.m scripts	.py scripts
Data setup	process_and_simulation_data.m	process_and_simulation_data.py
Plotting	MATLAB plotting	matplotlib

This makes the repository useful for:

• Translating control logic between environments
• Teaching implementation differences
• Verifying numerical consistency

---

🚀 Getting Started

MATLAB

1. Open MATLAB
2. Navigate to the matlab folder
3. Run any example:

Example_basic

---

Python

1. Create and activate a virtual environment
2. Install dependencies:

pip install -r requirements.txt

3. Run an example:

python3 Example_basic.py

---

🧠 Recommended Order

If you are new to the repository, a sensible progression is:

1. Basic example
2. MAN/AUTO transfer
3. Setpoint weighting
4. Feedforward and anti-windup
5. Gain scheduling
6. Selector control

---

⚠️ Notes

• This repository is script-based (not a packaged library)
• No license file is currently included
• MATLAB and Python implementations are maintained in parallel but independently
• Some auxiliary files (e.g., .mat, autosave files) are used for reproducibility

---

📚 Intended Use

This repository is especially useful for:

• Control engineering courses
• Research prototyping
• Algorithm validation
• Cross-language implementation comparison

---

🤝 Contributing

Contributions are welcome, especially:

• Improvements to numerical consistency between implementations
• Additional control scenarios
• Documentation enhancements

---

📄 License

No license has been defined yet. Add one before redistribution.