Stabilizing Receding Horizon Control of Sampled-Data Nonlinear Systems via Their Approximate Discrete-Time Models

Abstract Results on stabilizing receding horizon control of sampled-data nonlinear systems via their approximate discrete-time models are presented. The proposed receding horizon control is based on the solution of Bolza-type optimal control problems for the parametrized family of approximate discrete-time models. The sampling period is considered to be fixed, and the discretization parameter is allowed to vary. Sufficient conditions are established which guarantee that the controller that renders the origin to be asymptotically stable for the approximate model also stabilizes the exact discrete-time model for sufficiently small discretization parameters.

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