A framework is proposed for stochastic model predictive control of nonlinear systems with parametric uncertainty and time-varying disturbances. The Koopman operator is parameterized by polynomial chaos expansions, combining data-driven dynamics with uncertainty propagation. The resulting deterministic reformulation keeps the online optimization dimension dependent only on control horizon and input dimension.
Demonstrates how to combine data-driven dynamics models with principled uncertainty handling in control of complex systems.
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