Recursive Feasibility of Continuous-Time Model Predictive Control Without Stabilising Constraints

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