James B. Rawlings Research Group


John C. Campbell
Ph.D., University of Wisconsin-Madison, 1997

Publications

1
John C. Campbell and James B. Rawlings.
Predictive control of sheet- and film-forming processes.
AIChE J., 44(8):1713-1723, August 1998.

2
John C. Campbell.
Modelling, Estimation and Control of Sheet and Film Forming Processes.
PhD thesis, University of Wisconsin-Madison, May 1997.

3
Christopher V. Rao, John C. Campbell, James B. Rawlings, and Stephen J. Wright.
Efficient implementation of model predictive control for sheet and film forming processes.
In Proceedings of the American Control Conference, Albuquerque, NM, pages 2940-2944, 1997.

4
John C. Campbell and James B. Rawlings.
Evaluation of model predictive control performance in sheet and film forming processes.
Annual AIChE Meeting, Chicago, Illinois, November 1996.

5
John C. Campbell and James B. Rawlings.
Identification and control of sheet forming processes.
In IFAC '96 World Congress, San Francisco, California, pages 55-60, July 1-5, 1996.

6
John C. Campbell and James B. Rawlings.
Estimation and control of sheet and film forming processes.
In Irena Lasiecka and Blaise Morton, editors, Control Problems in Industry, pages 43-63, Boston, 1995. SIAM, Birkhäuser.

7
John C. Campbell and James B. Rawlings.
Gage control of film and sheet forming processes.
SIAM Conference on Control and its Applications, St. Louis, Missouri, April 1995.

Thesis Abstract

Modelling, Estimation and Control of Sheet and Film Forming Processes

The purpose of this study is to use new and existing control technology to improve product properties influenced by film forming processes. In any film forming process, there is an area at which the sheet of film is extruded through some flow controlling actuators and then pinned to roller conveyers. At some point a scanning sensor measures some properties of the film before it is rolled up on a drum as the final product. The sensor is mounted on a frame and scans back and forth over the film while the film is moving underneath it, and provides measurements in a zig-zag pattern along the sheet.

The work presented in this thesis advocates a constrained infinite horizon controller. The infinite horizon controller uses a process model and an initial state estimate, thus the modelling and estimation issues are of critical importance. A model structure is proposed that captures the main characteristics of the process including the scanning sensor and large time-delay. Model parameters are identified from process data, and symmetry is enforced to improve the parameter estimates.

The thickness of the film is estimated from sparse measurements using a periodic Kalman filter. The covariances of the estimates are used to evaluate scanning patterns. Since constant disturbance models can be used by the controller to achieve offset free control, a general disturbance model and a measured input disturbance model are presented. Lifted systems are constructed for both disturbance models. The lifted systems are useful for discussing the issues of observability and detectability. Conditions of detectability are established for both of the disturbance models.

Once the state of the film can be adequately reconstructed from the sensor data, a control strategy can be implemented to improve film properties. One important control contribution can be made by having the controller handle hard constraints on the actuators without employing clipping of the controller output signal. Determining targets and dealing with with regulator infeasibility are discussed as well. Finally, simulations bring modelling, estimation, and regulation together in an effort to better understand the periodic filter and disturbance models that are key to improved gage control.

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University of Wisconsin
Department of Chemical Engineering
Madison WI 53706