A new formulation of model-predictive control (MPC) for continuous-time nonlinear systems is I developed, which allows for the use of "real-time" (RT) optimization techniques in which the solution to the finite-horizon optimal control problem (OPC) evolves within the same timescale as the process dynamics. The computational savings of the RT solver are enhanced by the unique framework within which the OPC is posed, enabling significant reduction in the dimensionality of the search for situations where computational speed takes priority over optimality of the solutions. This framework, and its associated proof of stability, encompasses results, on sampled-data (SD) nonlinear model-predictive control (NMPC) implementation as a special case.