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Introduction

The effective control of dynamic systems is essential in numerous engineering and industrial applications. This report provides a comprehensive overview of key control strategies, ranging from classical techniques such as PID control to advanced methodologies including MPC and Reinforcement Learning. Beginning with fundamental concepts, the report explores various control architectures.

The discussion progresses to state-space approaches including pole placement and optimal control techniques like LQR and $H_{\infty}$ control. A significant portion is dedicated to MPC, highlighting both linear and nonlinear formulations, as well as robust variants designed to handle system uncertainties.

Then, a brief introduction to data-driven control such as RL is presented.
The report concludes with a summary of practical tools used in control system analysis and implementation.

Through theoretical discussion and illustrative examples, this report aims to equip the reader with a clear understanding of the diverse control strategies.