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

L. Grüne, D. Nesic: Stabilization of sampled-data nonlinear systems via their approximate models: an optimization based approach
in: Proceedings of the 41st IEEE Conference on Decision and Control, Las Vegas, Nevada, 2002, 1934 - 1939

DOI: 10.1109/CDC.2002.1184810
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Abstract:

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. In general, it is not true that a stabilizing controller for a discrete-time approximate model also stabilizes the exact sampled-data system, hence extra conditions are needed to ensure the desired behavior for the exact closed-loop system. In this paper we focus on the case when the sampling period T and the accuracy parameter h of the approximate discrete-time plant model are independent of each other and present appropriate conditions under which this approach yields practical and/or semiglobal stability of the exact discrete-time model.

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