6 citations · 10 across the 10 of their papers we have counts for
5 papers · 1 filter
A Koopman--Polynomial Chaos Framework for SMPC of Nonlinear Stochastic Processes
Wallace Gian Yion Tan, Efstathios Iliakis, Liang Wu +2
This paper develops a polynomial parametric Koopman operator (PPKO) framework for stochastic model predictive control (SMPC) of nonlinear systems with parametric uncertainty and ti…
Physics-Informed Neural Network Surrogates with Polynomial Chaos-Based Uncertainty Propagation for Stochastic Model Predictive Control
Srimanta Santra, Romi Patel, Saikat Mukherjee +2
Stochastic partial differential equations (PDEs) govern critical engineering and geophysical systems but are challenging to use for real-time control under parametric uncertainty.…
Polynomial Parametric Koopman Operators for Stochastic MPC
Efstathios Iliakis, Wallace Gian Yion Tan, Liang Wu +2
This paper develops a parametric Koopman operator framework for Stochastic Model Predictive Control (SMPC), where the Koopman operator is parametrized by Polynomial Chaos Expansion…
LQR for Systems with Probabilistic Parametric Uncertainties: A Gradient Method
Leilei Cui, Richard D. Braatz
A gradient-based method is proposed for solving the linear quadratic regulator (LQR) problem for linear systems with nonlinear dependence on time-invariant probabilistic parametric…
Koopman-BoxQP: Solving Large-Scale NMPC at kHz Rates
Liang Wu, Wallace Gian Yion Tan, Richard D. Braatz +1
Solving large-scale nonlinear model predictive control (NMPC) problems at kilohertz (kHz) rates on standard processors remains a formidable challenge. This paper proposes a Koopman…