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.
Cautious model predictive control using gaussian process regression
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2025 2verdicts
UNVERDICTED 2representative citing papers
BOW Planner applies constrained Bayesian optimization over reachable velocity windows to enable efficient, safe motion planning in complex environments with kinodynamic constraints.
citing papers explorer
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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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BOW: Bayesian Optimization over Windows for Motion Planning in Complex Environments
BOW Planner applies constrained Bayesian optimization over reachable velocity windows to enable efficient, safe motion planning in complex environments with kinodynamic constraints.