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

Abstract

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.