Model Predictive Control

Model Predictive Control (MPC) predicts how a system will evolve, optimizes a sequence of actions over a finite horizon, applies the first action, and replans as measurements arrive. Work spans linear and nonlinear optimization, sampling-based methods such as MPPI, and learned dynamics from Gaussian processes, Koopman models, and neural world models; these methods are used in robotics, autonomous vehicles, energy systems, and process control. Robust and stochastic MPC, control barrier functions, reachability, and distributed optimization address uncertainty, safety, and coordination, while GPU and embedded solvers aim to meet real-time limits. A central tension is that longer, richer predictions can improve planning but increase computation and sensitivity to model error, so practical controllers must balance performance, safety guarantees, and reliable execution.