AutoFuzzy Library for Arduino

Overview

AutoFuzzy is a powerful and flexible Arduino library that provides automated fuzzy logic capabilities with self-optimizing features. The library is designed to simplify the implementation of fuzzy logic controllers while offering advanced features for optimization and automation.

Features

• Easy-to-use API for fuzzy logic implementation
• Support for multiple input and output variables
• Triangular and trapezoidal membership functions
• Automated parameter optimization using genetic algorithms
• Up to 10 variables (inputs + outputs)
• Up to 5 membership functions per variable
• Up to 50 fuzzy rules
• Memory-efficient implementation
• Built-in constraint handling
• Real-time evaluation capabilities

Installation

1. Download the library as a ZIP file
2. In the Arduino IDE, go to Sketch > Include Library > Add .ZIP Library
3. Select the downloaded ZIP file
4. Restart the Arduino IDE

Basic Usage

Include the Library

#include <AutoFuzzy.h>

Create an Instance

AutoFuzzy fuzzy;

Define Variables

// Add input variable (name, min value, max value)
fuzzy.addInput("temperature", 0, 100);

// Add output variable (name, min value, max value)
fuzzy.addOutput("fan_speed", 0, 255);

Define Membership Functions

// Triangular membership function (variable, name, left, center, right)
fuzzy.addTriangularMF("temperature", "cold", 0, 20, 40);
fuzzy.addTriangularMF("temperature", "hot", 60, 80, 100);

// Trapezoidal membership function (variable, name, left, left-center, right-center, right)
fuzzy.addTrapezoidalMF("fan_speed", "slow", 0, 0, 50, 127);
fuzzy.addTrapezoidalMF("fan_speed", "fast", 127, 200, 255, 255);

Define Rules

fuzzy.addRule("temperature", "cold", "fan_speed", "slow");
fuzzy.addRule("temperature", "hot", "fan_speed", "fast");

Optimize and Evaluate

// Optional: Run optimization (iterations)
fuzzy.autoOptimize(100);

// Evaluate input and get output
float temp = analogRead(A0) * 100.0 / 1023.0;
float fanSpeed = fuzzy.evaluate(&temp);

Example Projects

1. Simple LED Brightness Control

Controls LED brightness based on ambient light level.

// See examples/SimpleLED/SimpleLED.ino

2. Plant Watering System

Automated watering system based on soil moisture and temperature.

// See examples/PlantWatering/PlantWatering.ino

3. HVAC Control System

Advanced temperature and humidity control with energy optimization.

// See examples/HVACControl/HVACControl.ino

Advanced Usage

Multiple Inputs

fuzzy.addInput("temperature", 0, 100);
fuzzy.addInput("humidity", 0, 100);
fuzzy.addOutput("fan_speed", 0, 255);

// Evaluate multiple inputs
float inputs[] = {temperature, humidity};
float fanSpeed = fuzzy.evaluate(inputs);

Optimization Parameters

The autoOptimize() function uses a genetic algorithm to optimize membership function parameters:

// Default optimization
fuzzy.autoOptimize(100);  // 100 iterations

// The optimization process:
// 1. Randomly adjusts membership function parameters
// 2. Maintains valid ranges and relationships
// 3. Preserves system stability
// 4. Improves response based on built-in fitness function

Memory Usage

• Each variable: ~32 bytes
• Each membership function: ~24 bytes
• Each rule: ~4 bytes
• Maximum memory usage with default settings: ~1KB

Limitations

• Maximum 10 variables (combined inputs and outputs)
• Maximum 5 membership functions per variable
• Maximum 50 rules
• No support for custom membership function shapes
• Single output evaluation per call

Performance Considerations

• Evaluation time increases linearly with number of rules
• Optimization process can take significant time
• Consider reducing iteration count for faster optimization
• Memory usage increases with number of variables and rules

Troubleshooting

Common Issues

1. System not responding:

   • Check if variables are within defined ranges
   • Verify rule definitions
   • Ensure proper connection between inputs and outputs

2. Unexpected behavior:

   • Verify membership function overlap
   • Check rule consistency
   • Validate input scaling

3. Memory issues:

   • Reduce number of variables/rules
   • Optimize membership function count
   • Consider using PROGMEM for constant data

Best Practices

1. System Design:

   • Start with simple systems and gradually add complexity
   • Use appropriate ranges for variables
   • Ensure smooth overlap between membership functions
   • Design rules to cover all possible scenarios

2. Optimization:

   • Run optimization during setup if possible
   • Use appropriate iteration count for your application
   • Monitor system performance after optimization
   • Consider periodic re-optimization for dynamic systems

3. Memory Management:

   • Use minimum necessary membership functions
   • Optimize rule count
   • Clean up unused variables and rules

Contributing

1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Create a new Pull Request

License

This library is released under the MIT License. See the LICENSE file for details.

Support

For bugs, feature requests, and discussions:

• Open an issue on GitHub
• Contact: https://azzar.netlify.app

Version History

• 1.0.0 (2024-12-13)
  • Initial release
  • Basic fuzzy logic functionality
  • Automated optimization features
  • Example projects

Future Development

• Support for custom membership function shapes
• Advanced optimization algorithms
• Multiple output evaluation
• Enhanced data logging and analysis tools