The content discusses the application of extended dynamic mode decomposition (EDMD) in data-driven model predictive control (MPC) for stability analysis. It explores the theoretical foundation, numerical simulations, and key results regarding stability guarantees using surrogate models.
The study focuses on proving practical asymptotic stability for controlled equilibriums in MPC systems utilizing EDMD-based models. It highlights the importance of cost controllability and error bounds in ensuring stable closed-loop performance.
Key points include the theoretical background of MPC, the use of EDMD for surrogate modeling, error bounds analysis, and numerical simulations validating the proposed stability guarantees. The content emphasizes the significance of preserving cost controllability and achieving practical asymptotic stability in data-driven MPC applications.
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