Efficient Data-Driven Predictive Control of Nonlinear Chemical Processes using Reduced-Order Koopman Modeling
A data-driven reduced-order Koopman modeling and predictive control approach is proposed to efficiently regulate the operation of nonlinear chemical processes.
