This repository contains the curriculum materials used for the Intelligent Control Systems course YTU Department of Control and Automation Eng.

The lecture on intelligent control systems explains intelligence-based control systems that can be used to steer complicated systems while overcoming modelling challenges. Expert systems, machine learning, and deep learning are a few examples of intelligent control systems. These systems use a high-level decision-making process to generate the control signal based on a qualitative or heuristic understanding of the process. Computational Thinking Tools for Control Engineers, Dynamical System Modeling, Model Predictive Control (MPC), Data-driven modelling, Data-driven methods, and Data-driven control techniques are all included in intelligent control systems. Additionally, general applications of system modelling and control design, thermal systems, robotic control systems, and control system performance are covered.

In addition to learning how to use intelligent systems to manage complicated designed systems, students will examine the principles that underlie them. Students must use computationally effective tools to analyze dynamics and control systems in order to evaluate the practical concerns for experimental or simulated structures. Students must finish a specific project, assess system performance, and have a comprehensive understanding of various data-driven modelling, data-driven methodologies, and data-driven control applications.

I would like to express my gratitude to the students of the Intelligent Control Systems course of the YTÜ Control and Automation Engineering department, Class of Fall 2022, whose dedication and hard work made this project possible. I am also deeply thankful to Doctors Marco Rossi, Julia Hoerner, and Melda Ulusoy for their invaluable contributions.