A classical feedback control method widely used in robotics and industrial automation. It computes a control signal based on the proportional error, the accumulated integral of past errors, and the derivative predicting future error trends. PID controllers are valued for their simplicity, robustness, and ease of tuning, making them suitable for motor control, joint position regulation, and velocity stabilization. Despite their age, PID controllers remain foundational in robotics, especially in low-level control loops. However, they rely on careful parameter tuning and assume relatively stable system dynamics. In modern robotics systems, PID control is often combined with higher-level planners or learning-based approaches. Understanding PID is essential for grasping how traditional control contrasts with machine learning-driven robotics.