Proves recursive feasibility and asymptotic stability for data-driven Koopman MPC with terminal conditions under a proportional error bound, applicable via kEDMD to broad nonlinear systems and shown in a numerical example.
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Develops a nonparametric sparse online algorithm to learn the Koopman operator iteratively via stochastic approximation with explicit complexity control and convergence guarantees in misspecified RKHS settings via conditional mean embeddings.
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Stability of data-driven Koopman MPC with terminal conditions
Proves recursive feasibility and asymptotic stability for data-driven Koopman MPC with terminal conditions under a proportional error bound, applicable via kEDMD to broad nonlinear systems and shown in a numerical example.
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Nonparametric Sparse Online Learning of the Koopman Operator
Develops a nonparametric sparse online algorithm to learn the Koopman operator iteratively via stochastic approximation with explicit complexity control and convergence guarantees in misspecified RKHS settings via conditional mean embeddings.