Master Thesis Research
Comprehensive documentation of my master's thesis research at Yildiz Technical University, bridging Deep Learning and Control Theory.
Welcome to the Master Thesis Research section of my portfolio. Here, you will find comprehensive documentation of my academic journey and research conducted at Yildiz Technical University within the Control and Automation Engineering department.
My thesis, titled “Deep Learning Based Model Predictive Control of Nonlinear Systems”, explores the intersection of modern artificial intelligence and classical control theory. It aims to develop highly efficient, predictive models that can handle complex nonlinear dynamics — contributing to the evolution of intelligent, automated systems. This section serves as a living log of my problem definitions, mathematical modeling, dataset preparation, and key findings.
Thesis Description & Objectives
A detailed overview of the research problem, the limitations of traditional Model Predictive Control (MPC) in nonlinear systems, and how Deep Learning provides a robust solution.
Dataset Generation & Preparation
An in-depth look into how the dataset for nonlinear system dynamics was meticulously generated, curated, and preprocessed to ensure the validity and reliability of the neural network models.