US 7,454,253 B2 · 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.

Claims25

Dependent claims

A method of fast approximating the performance of a cross-direction machine-direction multivariable model predictive controller (CDMD MPC) for a spatially-distributed sheet process that comprises the steps of:

(a) identifying a process model for the spatially-distributed sheet process which is employed in a sheetmaking machine that forms a sheet of material and that comprises:
(i) at least two sets of actuator arrays each distributed adjacent the material in the cross direction (CD), wherein each set of actuator arrays is controllable to vary properties of the material;
(ii) means for measuring and acquiring properties data about the properties of the material in the cross direction;
(iii) CDMD MPC for the spatially-distributed sheet process providing CD control to the multiple-array cross-directional process, wherein the MPC, in response to signals that are indicative of the properties data, provides signals to the at least two sets of actuator arrays to vary properties of the material;
and
(iv) means for fast approximating the performance of the CDMD MPC, based on the process model for the spatially-distributed sheet process and tuning parameters and constraints, that includes means for calculating a steady-state spatial performance of the CDMD MPC and means for calculating a dynamic performance of the CDMD MPC;
(b) inputting tuning parameters and constraints into the CDMD MPC;
(c) calculating steady-state spatial performance for the CDMD MPC for different sheet property modes that includes CD only mode, machine-direction (MD) only mode, and CD and MD mode;
(d) calculating a dynamic performance for the CDMD MPC;
(e) inputting optimal tuning parameters to the CDMD MPC;
and
(f) controlling the sheetmaking machine with the CDMD MPC.

9 more elements

In a process control system having a cross-direction machine-direction multivariable model predictive controller (CDMD MPC) for providing control to a spatially-distributed sheet process which is employed in a sheetmaking machine that includes at least one manipulated actuator array and at least one controlled measurement array and that forms a sheet of material and, a method for providing control of the spatially-distributed sheet process that comprises the steps of:

(a) identifying a process model for the spatially-distributed sheet process;
(b) inputting tuning parameters and constraints into the CDMD MPC while operating the sheetmaking machine;
(c) calculating a steady-state spatial performance for the CDMD MPC for different sheet property modes that includes CD only mode, machine-direction (MD) only mode, and CD and MD mode while operating the sheetmaking machine;
(d) calculating a dynamic performance for the CDMD MPC while operating the sheetmaking machine;
(e) comparing the calculated spatial and dynamic performance to a desired level of spatial and dynamic performance;
(f) repeating steps (b) to (e) using different tuning parameters with constraints until the calculated spatial and dynamic performance meets or exceeds the desired level of spatial and dynamic performance;
(g) inputting optimal tuning parameters to the CDMD MPC;
and
(h) controlling the spatially-distributed sheet process with the CDMD MPC while operating the sheetmaking machine.

6 more elements

Dependencies are inferred from the claim text.

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

Life of this patent

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

Life of this patent

Abstract

Claims25