An advanced control strategy that optimizes future system behavior over a finite time horizon. At each control step, MPC uses a mathematical model of the robot to predict future states and select control actions that minimize a cost function while respecting constraints. This makes MPC particularly effective for complex robotic systems with physical limits, such as joint bounds or collision avoidance. MPC is widely used in legged robots, autonomous vehicles, and manipulation tasks. Compared to PID control, MPC can anticipate future dynamics rather than reacting only to current errors. Its main drawbacks are computational cost and reliance on accurate models. MPC represents a bridge between traditional control theory and modern optimization-based robotics.