EEE Project 2: GA Fuzzy PID controller for DC motor control

Summary

This video demonstrates a project where a DC motor's speed is controlled using a fuzzy PID controller, with its parameters optimized through a Genetic Algorithm (GA). The script explains the design and working of the fuzzy logic controller, the optimization process using GA, and how the best parameter values are determined. Viewers can observe the optimization progress and the output improvements over time. The project is aimed at providing an effective solution for DC motor control, and users can contact the creators for the full project files.

Takeaways

• 😀 The project demonstrates how to use a fuzzy PID controller to control a DC motor.
• 😀 Genetic Algorithm (GA) is used to optimize the parameters of the fuzzy logic controller.
• 😀 The optimization process seeks to find the best possible values for the fuzzy logic controller's parameters.
• 😀 The fuzzy logic controller uses two main inputs: error and change in error.
• 😀 The optimized fuzzy controller output aims to reduce the error and achieve better motor control.
• 😀 The Genetic Algorithm minimizes the Integral of Time Absolute Error (ITAE) to improve system performance.
• 😀 The optimization process is visually represented with graphs showing the error reduction over time.
• 😀 The Genetic Algorithm iteratively adjusts the parameters to find the most optimal solution.
• 😀 Viewers can contact the creators via email to obtain the project file or purchase the complete project.
• 😀 The video serves as a demonstration, showing that the project file is working correctly and optimized.

Q & A

What is the main objective of this project?

The main objective of this project is to optimize the parameters of a fuzzy PID controller for controlling the speed of a DC motor using a Genetic Algorithm (GA).

How does the fuzzy logic controller function in this project?

The fuzzy logic controller in this project takes error and change in error as inputs and produces an output that adjusts the DC motor's speed, aiming for optimal control based on these variables.

Why is Genetic Algorithm (GA) used in this project?

GA is used to optimize the parameters of the fuzzy PID controller because it can efficiently search through a large parameter space and find the best values that minimize error and improve the motor control response.

What is the role of the objective function in the GA optimization process?

The objective function in the GA optimization process is the Absolute Error (AE) function, which the algorithm aims to minimize. The goal is to find parameter values that lead to the least error between the desired and actual motor speed.

What does the 'pink color near to one' represent in the optimization process?

The 'pink color near to one' indicates that the GA is approaching the optimal solution, meaning the error is close to being minimized, and the fuzzy logic controller is performing optimally.

What does the optimization process visually show in the project?

The optimization process is visualized in a graph that shows the system's response over time. The graph tracks the objective function's value, which decreases as the GA optimizes the parameters of the fuzzy PID controller.

What are the inputs to the fuzzy logic controller in this project?

The inputs to the fuzzy logic controller are 'error' and 'change in error,' which help determine how to adjust the motor's speed.

What type of motor is used in this project for control?

A DC motor is used in this project to demonstrate the fuzzy logic controller's ability to regulate speed through optimized parameters.

What is the significance of the 'fuzzy PID controller' in this project?

The fuzzy PID controller is used to adjust the motor speed by applying fuzzy logic to traditional PID control principles. It enhances control accuracy by considering both the current error and the rate of change in error.

How can viewers obtain the project file or purchase the full project?

Viewers can obtain the project file or purchase the complete project by contacting the project team via email at [email protected].