Set-Valued and Lyapunov Methods for MPC

Model predictive control (MPC), sometimes referred to as the receding horizon control, is an optimization-based approach to stabilization of discrete-time control systems. It is well-known that infinite-horizon optimal control, with the Linear-Quadratic Regulator [1] as the fundamental example, can provide optimal controls that result in asymptotically stabilizing feedback [8].

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