Model Predictive Control Explained for Real-Time Robot Navigation

Model Predictive Control (MPC) is a technique that repeatedly predicts a robot's future states and selects the best control inputs over a short time horizon. Unlike traditional controllers, MPC applies only the first action in an optimized sequence before recalculating, allowing continuous adaptation. Its cost function balances path tracking accuracy, control effort, motion smoothness, and obstacle avoidance. While more computationally demanding than simple feedback controllers, MPC handles vehicle dynamics, input constraints, and obstacles in a unified framework. It is especially suited for robots where basic point-to-point control falls short, though fallback behavior is essential if the optimizer misses its deadline.