Stabilization of sampled-data nonlinear systems via their approximate models: an optimization based approach

We present results on numerical regulator design for sampled-data nonlinear plants via their approximate discrete-time plant models. The regulator design is based on an approximate discrete-time plant model and is carried out either via an infinite horizon optimization problem or via a finite horizon with terminal cost optimization problem. We focus on the case when the sampling period and the accuracy parameter h of the approximate discrete-time plant model are independent of each other and show that with this approach practical and/or semiglobal stability of the exact discrete-time model is achieved under appropriate conditions.