  We consider sampled-data Model Predictive Control (MPC) of nonlinear
continuous-time control systems. We derive sufficient conditions to guarantee
recursive feasibility and asymptotic stability without stabilising costs and/or
constraints. Moreover, we present formulas to explicitly estimate the required
length of the prediction horizon based on the concept of (local) cost
controllability. For the linear-quadratic case, cost controllability can be
inferred from standard assumptions. In addition, we extend results on the
relationship between the horizon length and the distance of the initial state
to the boundary of the viability kernel from the discrete-time to the
continuous-time setting.
