Fast performance prediction of multivariable model predictive controller for paper machine cross-directional processes

Abstract

A technique for fast performance prediction of multivariable model predictive controllers (MPC) for large-scale spatially-distributed dynamic systems is provided. When operating an MPC for a two-dimensional sheetmaking process, the controller's performance can be separated into spatial performance and dynamic performance. The steady-state spatial performance for different sheet property modes is first predicted, thereafter, the dynamic performance is predicted on the basis of the steady-state actuator profiles. During the steady-state spatial performance prediction, the original MPC's cost function is approximated to be a steady-state cost function. Then the steady-state measurement profiles and the actuator profiles are calculated through the steady-state cost function. The actuator profiles in time series can be efficiently solved by minimizing the difference between the actuator profiles and the steady-state actuator profiles in the face of constraints. Finally, the model can be employed to expeditiously compute the measurement profiles in times series.