6 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…
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…
Least-Squares Multi-Step Koopman Operator Learning for Model Predictive Control
Liang Wu, Wallace Gian Yion Tan, Leqi Zhou +2
MPC is widely used in real-time applications, but practical implementations are typically restricted to convex QP formulations to ensure fast and certified execution. Koopman-based…
EIQP: Execution-time-certified and Infeasibility-detecting QP Solver
Liang Wu, Wei Xiao, Richard D. Braatz
Solving real-time quadratic programming (QP) is a ubiquitous task in control engineering, such as in model predictive control and control barrier function-based QP. In such real-ti…
A Parallel Vector-form Decomposition for Accelerating Execution-time-certified -penalty Soft-constrained MPC
Liang Wu, Liwei Zhou, Richard D. Braatz
Handling possible infeasibility and providing an execution time certificate are two pressing requirements of real-time Model Predictive Control (MPC). To meet these two requirement…
An Execution-time-certified Riccati-based IPM Algorithm for RTI-based Input-constrained NMPC
Liang Wu, Krystian Ganko, Shimin Wang +1
Establishing an execution time certificate in deploying model predictive control (MPC) is a pressing and challenging requirement. As nonlinear MPC (NMPC) results in nonlinear progr…