Control theory is a foundational discipline in robotics focused on designing systems that regulate the behavior of dynamic processes. It provides mathematical tools to ensure stability, accuracy, and robustness when controlling robotic motion and interaction with the environment. Traditional robotics heavily relies on control theory to compute actuator commands based on system states, sensor feedback, and desired trajectories. Classical approaches include PID controllers, state-space models, and optimal control methods. Control theory assumes well-defined system models and predictable dynamics, which makes it effective for structured and deterministic environments. However, its limitations become apparent in complex or uncertain scenarios, where modeling errors and external disturbances are difficult to handle. Despite this, control theory remains a core component of many robotic systems and is often combined with learning-based methods in modern hybrid approaches.