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MATLAB code for GA Tuning of PI controller using Chat GPT | MATLAB code for GA Tuning PI controller

MATLAB code for GA Tuning of PI controller using Chat GPT | MATLAB code for GA Tuning PI controller

In this simulation, we'll explore the process of tuning the proportional-integral (PI) controller parameters, Kp and Ka, for a given transfer function using MATLAB. What makes this tutorial unique is that we'll be using ChatGPT to generate the code for the genetic algorithm-based optimization of the PI controller.

The ChatGPT Interaction:

We begin by interacting with ChatGPT and providing a prompt to generate the MATLAB code for tuning the PI controller. The prompt includes details about the plant transfer function and specifies the use of a genetic algorithm for optimization.

Code Generation and Review:

After interacting with ChatGPT, we receive MATLAB code tailored for our problem. The generated code includes instructions to define the plant transfer function, set up the PI controller, calculate the objective function (fitness), and execute the genetic algorithm.

Modifying the Code:

Upon reviewing the code, we notice a small issue with the fitness function, as it returns an array of values. To address this, we modify the fitness function to calculate the mean of the error array, ensuring compatibility with MATLAB's genetic algorithm functions.

Executing the Code:

With the corrected code, we execute the MATLAB script. The genetic algorithm optimizes the PI controller parameters, providing the optimal values for Kp and Ka.


Using ChatGPT to generate MATLAB code for tuning the PI controller parameters with a genetic algorithm demonstrates the power of AI-driven assistance in engineering tasks. This approach streamlines the code development process and allows engineers to focus on refining and implementing the generated code for efficient control system tuning.

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