Model Predictive Control
Model Predictive Control (MPC) is an advanced control strategy that uses a dynamic model of a system to predict its future behavior and optimize control actions over a finite time horizon. It solves an optimization problem at each time step to determine the best control inputs while satisfying constraints on states, inputs, and outputs. MPC is widely used in process industries, robotics, and autonomous systems due to its ability to handle multi-variable systems and constraints explicitly.
Developers should learn MPC when working on control systems for applications like chemical processes, autonomous vehicles, robotics, or energy management, where handling constraints and optimizing performance over time is critical. It is particularly useful in scenarios requiring real-time optimization, such as predictive maintenance, trajectory planning, or resource allocation, as it provides a systematic framework for decision-making under uncertainty and dynamic conditions.