Model Predictive Control

Model predictive control (MPC) is an advanced control strategy that uses a dynamic model of the system to predict future behavior and optimize control actions over a finite horizon. At each time step, the controller solves an optimization problem to find the best sequence of future actions, applies the first action, and repeats the process — a receding horizon approach that allows the controller to handle constraints, nonlinearities, and changing objectives. MPC is the natural partner to system identification: the better the model, the better the predictions, and the better the control. The systems-theoretic significance is that MPC treats control as an ongoing conversation between the model and the world, rather than a one-time design. The controller that cannot update its model is not a controller but a monument to an outdated assumption. See also: Control Theory, Optimization, Adaptive Control\n\n\n\n