PID-ANN-Fuzzy-SumoBot

Implementing PID, ANN, and Fuzzy Logic Controllers for a DC Encoder Motor in a SUMO Robot

Overview

This project explores the implementation of PID, Artificial Neural Network (ANN), and Fuzzy Logic Controllers to control a DC encoder motor in a SUMO Robot. The aim is to analyze and compare the performance of these controllers in terms of stability, responsiveness, and adaptability. The project is implemented using MATLAB Simulink and C++ for real-time simulation and optimization.

Key Features

✔ DC Motor Modeling & System Identification
✔ Implementation of PID, ANN, and Fuzzy Controllers
✔ MATLAB Simulink Simulation & Performance Evaluation
✔ C++ Implementation for PID Control
✔ Comparative Analysis of Different Control Strategies

Project Structure

📂 PID-ANN-Fuzzy-SumoBot  
 ├── 📁 Models/          # MATLAB Simulink models  
 ├── 📁 Reports/         # Documentation and performance analysis  
 ├── README.md          # Project overview  
 └── LICENSE            # License file  

Getting Started

Requirements

• MATLAB & Simulink (R2021 or later)
• Control System Toolbox
• Simulink Control Design
• C++ Compiler (for PID implementation)

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/PID-ANN-Fuzzy-SumoBot.git
   cd PID-ANN-Fuzzy-SumoBot

2. Open MATLAB and navigate to the project directory.
3. Run the provided Simulink models and scripts.
4. Compile and execute the C++ PID Controller (if applicable).

Results & Comparisons

Controller	Settling Time	Overshoot	Disturbance Handling	Adaptability
PID	Moderate	High	Weak	Limited
ANN	Fast	Low	Strong	High
Fuzzy	Moderate	Low	Strong	Adaptive

Contributors

Nada Zein Eddin

Wa’el Jad Allah

Esra’a Tanashat

License

This project is licensed under the MIT License – see the LICENSE file for details.