US 10,459,428 B2 · Optimal closed-loop input design for identification of flat-sheet process models

Abstract

Sheetmaking cross-directional (CD) control requires a sophisticated model-based controller whose operation requires an accurate model of process behavior, but due to the complexity of the process, identifying these process models is challenging. Current techniques rely on open-loop process experimentation. Using non-causal scalar transfer functions for the steady-state CD process model and controller model avoid the problem of large dimensions associated with the CD process. These non-causal transfer functions can be represented by causal transfer functions that are equivalent to the non-causal ones in the sense of the output spectrum. A closed-loop optimal input design framework is proposed based on these causal equivalent models. CD actuators have responses in both sides along the cross direction which can be viewed as a non-causal behavior. Techniques to perform the non-causal modeling are demonstrated and developed in a closed-loop optimal input design framework based on non-causal modeling of the closed-loop CD process.

Claims20

Dependent claims

A method of closed-loop identification of process models for the model predictive control (MPC) by a profile analyzer, of an industrial sheetmaking system having a plurality of actuators arranged in the cross-direction (CD) and a scanning sensor supported on a supporting frame wherein the profile analyzer manipulates the actuators to control the sheet parameters in a spatially distributed sheet process, the method comprises the steps of:

(a) receiving from the scanning sensor, data indicative of the magnitude of a measured sheet property of the spatially-distributed sheet process;
(b) selecting by the profile analyzer a process model for the spatially-distributed sheet process wherein the process model is defined by a matrix;
(c) converting the matrix into a non-causal transfer function;
(d) converting the non-causal transfer function into a causal model that has an equivalent spectrum;
(e) using the causal model to design an optimal input spectrum for process excitation;
(f) transforming a frequency domain representation of the optimal input spectrum to a time domain realization which is a sequence of actuator movements;
(g) providing signals by the profile analyzer to the plurality of actuators, of the sequence of actuator movements and collecting data via the scanning sensor changes in cross-directional sheet properties due to the sequence of actuator movements to determine measured actuator response profiles;
(h) analyzing by the profile analyzer, the data to extract new model parameters;
and
(i) using the new model parameters for selecting the process model.

7 more elements

A profile analyzer for providing multivariable model predictive control (MPC) to a cross-direction (CD) process having at least one manipulated actuator array and a scanning sensor supported on a supporting frame, and at least one controlled measurement array, wherein the profile analyzer manipulates the actuators to control the sheet parameters in a spatially distributed sheet process the profile analyzer comprises a processor that is configured to:

receive from the scanning sensor, data indicative of the magnitude of a measured sheet property of the spatially-distributed sheet process;
select a process model for the spatially-distributed sheet process wherein the process model is defined by a matrix;
convert the matrix into a non-causal transfer function;
convert the non-causal transfer function into a causal model that has an equivalent spectrum;
use the causal model to design an optimal input spectrum for process excitation;
transform a frequency domain representation of the optimal input spectrum to a time domain realization which is a sequence of actuator movements;
provide, signals to the plurality of actuators, of the sequence of actuator movements and collecting data via the scanning sensor changes in cross-directional sheet properties due to the sequence of actuator movements to determine measured actuator response profiles;
analyze the data to extract new model parameters;
and
use the new model parameters for the process model.

7 more elements

Dependencies are inferred from the claim text.

Optimal closed-loop input design for identification of flat-sheet process models

Life of this patent

Optimal closed-loop input design for identification of flat-sheet process models

Life of this patent

Abstract

Claims20