PID Fuzzy Logic Controller for DC Motor Speed

Implement Fuzzy Logic Control and Fuzzy Inference System for motor speed management

Project Overview

This project involves the development of a Fuzzy Logic Controller (FLC) and Fuzzy Inference System (FIS) to manage the speed of a motor in Revolutions Per Minute (RPM). The system measures current error and delta error, utilizing predefined membership functions and rules to control the motor’s speed. The computation is performed in one forward pass only.

Project Details

  • Developer: Gredynov Sitanggang, MSc student in Electronics and Instrumentation, Universitas Gadjah Mada; Haydar Amru Revanda, BSc student in Electronics and Instrumentation, Universitas Gadjah Mada
  • Course Context: This project was based on the mid-term exam project of the Control System course taught by Dr. Oskar Natan, S.ST., M.Tr.T., Ph.D.

Key Features

  1. Affordable hardware setup using ESP32 microcontrollers
  2. Real-time data monitoring and analysis through ThingSpeak platform
  3. Early warning system capabilities
  4. Potential for scalability and wide-scale deployment

Initial Implementation

  • Inputs: Current error and delta error.
  • Output: Motor speed (RPM).
  • Fuzzy Logic Control: Utilizes membership functions and a rule base to compute the control action.

Incremental Error Input

  • Addition of Incremental Error: An incremental error input was added to enhance the precision of the control system.
  • Function: The incremental error helps to fine-tune the control output by considering the rate of change of the error, providing a more responsive and accurate control.

Future Developments

  • More Fuzzy Sets: Plans to add more fuzzy sets to each fuzzy variable, increasing the granularity of the control system.
  • MIMO Control: Extending the system to Multi Inputs Multi Outputs (MIMO), for instance, controlling voltage excitation for UVW lines of a 3-phase motor.

Potential Impact

The addition of the incremental error input significantly improves the performance of the fuzzy logic controller and making it more adaptive and precise in controlling the motor speed. This enhancement showcases a practical application of advanced control theory in real-world systems, contributing to the development of more efficient and responsive motor control mechanisms.

Access the Code

The code for this project can be accessed here.