Exponential stability of data-driven nonlinear MPC based on input/output models

Lea Bold, Irene Schimperna, Karl Worthmann, Johannes K\"ohler

We consider nonlinear model predictive control (MPC) schemes using surrogate models in the optimization step based on input-output data only. We establish exponential stability for sufficiently long prediction horizons assuming exponential stabilizability and a proportional error bound. Moreover, we verify the imposed condition on the approximation using kernel interpolation and demonstrate the practical applicability to nonlinear systems with a numerical example.

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