Model predictive control
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Model predictive control (MPC) is an optimization-based control strategy that uses a mathematical model of a system to predict its future behavior and repeatedly solve for the best sequence of control actions over a finite time horizon. At each control step, MPC computes an optimal input by minimizing a cost function — typically penalizing tracking error and control effort — subject to constraints on states and actuator limits, applies only the first action, then re-solves the problem at the next timestep in a receding-horizon fashion. In robotics and AI, MPC is widely applied to legged locomotion, aerial vehicles, robotic arms, mobile robots, and surgical systems, enabling real-time handling of complex dynamics, physical constraints, and obstacle avoidance. It naturally accommodates nonlinear dynamics, uncertainty, and multi-robot coordination. Modern variants integrate learned models, neural networks, and safety constraints such as control barrier functions to extend its capabilities. MPC matters because it provides a principled, systematic framework for achieving high-performance, constraint-aware control in systems where safety, precision, and adaptability are critical — bridging the gap between offline trajectory planning and reactive real-time execution.
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