AbstractControl design often seeks the best trajectory along which to move a system from its current state to a target state. Most control methods consider only the first step of the full trajectory toward the target state. Model predictive control considers the full sequence of steps required to move the system optimally from its current state to a future target. The control system then applies the first inputs to start the system along that optimal trajectory. However, rather than continuing, the system takes new inputs and recalculates a new optimal trajectory based on its updated information. The system then begins to move along the new trajectory, updates again, and adjusts its trajectory again. By repeated updating, the system can often perform very well with limited information about nonlinear dynamics and other uncertainties.