James B. Rawlings Research Group

Kenneth R. Muske
Ph.D., University of Texas at Austin, 1995

Publications

1
Kenneth R. Muske.
Linear Model Predictive Control of Chemical Processes.
PhD thesis, The University of Texas at Austin, January 1995.

2
Edward S. Meadows, Kenneth R. Muske, and James B. Rawlings.
Implementable model predictive control in the state space.
In Proceedings of the 1995 American Control Conference, pages 3699-3703, 1995.

3
Kenneth R. Muske and James B. Rawlings.
Nonlinear moving horizon state estimation.
In Ridvan Berber, editor, Methods of Model Based Process Control, pages 349-365. Kluwer, 1995.

4
James B. Rawlings, E. Scott Meadows, and Kenneth R. Muske.
Nonlinear model predictive control: a tutorial and survey.
In ADCHEM '94 Proceedings, Kyoto, Japan, pages 185-197, 1994.

5
Kenneth R. Muske, E. Scott Meadows, and James B. Rawlings.
The stability of constrained receding horizon control with state estimation.
In Proceedings of the 1994 American Control Conference, pages 2837-2841, 1994.

6
James B. Rawlings and Kenneth R. Muske.
Stability of constrained receding horizon control.
IEEE Trans. Auto. Cont., 38(10):1512-1516, October 1993.

7
Edward S. Meadows, Kenneth R. Muske, and James B. Rawlings.
Constrained state estimation and discontinuous feedback in model predictive control.
In Proceedings of the 1993 European Control Conference, pages 2308-2312, 1993.

8
Kenneth R. Muske, James B. Rawlings, and Jay H. Lee.
Receding horizon recursive state estimation.
In Proceedings of the 1993 American Control Conference, pages 900-904, 1993.

9
Kenneth R. Muske and James B. Rawlings.
Linear model predictive control of unstable processes.
J. Proc. Cont., 3(2):85-96, May 1993.

10
James B. Rawlings and Kenneth R. Muske.
An overview of model predictive control.
Annual AIChE Meeting, St. Louis, Missouri, November 1993.

11
Kenneth R. Muske and James B. Rawlings.
Model predictive control with linear models.
AIChE J., 39(2):262-287, February 1993.

12
Kenneth R. Muske and James B. Rawlings.
Implementation of a stabilizing constrained receding horizon regulator.
In Proceedings of the 1992 American Control Conference, pages 1594-1595, 1992.

13
Jay H. Lee, Doug Robertson, Kenneth R. Muske, and James B. Rawlings.
Constrained receding horizon estimation.
Annual AIChE Meeting, Miami Beach, Florida, November 1992.

Thesis Abstract

Linear Model Predictive Control of Chemical Processes

Model predictive control has become one of the dominant methods of chemical process control in terms of successful industrial applications and as a focus of academic research. The relevant articles in the engineering literature, which range in scope from descriptions of industrial applications to theoretical analyses, clearly illustrate this fact. Most of the applications have been based on the implementations of linear model predictive control developed by the process industries to control constrained, multivariable chemical processes. The emphasis in the development of these controllers was a robust algorithm with acceptable performance that could be implemented on-line. Therefore, several aspects of these controllers were designed based on a heuristic approach with little theoretical justification. This design philosophy produced controllers that performed very well for certain processes, but were unable to adequately address others. After more than a decade of experience with this technology, these limitations have become apparent.

This study presents a theoretical analysis of linear model predictive control that addresses these limitations. By exploiting the features common to both linear model predictive control and linear quadratic regulator/estimator theory, many of the heuristic design features that have restricted the technology are removed. Linear state-space models are used to represent the process to address unstable as well as stable plants. The incorporation of a stabilizing, constrained receding horizon regulator guarantees nominal stability for all valid tuning parameters and eliminates the need to tune for nominal stability. Output feedback is performed using linear state estimation techniques. These techniques provide increased flexibility in the design of the disturbance model for the process within a well-established framework. Off-set free control also can be guaranteed with the use of these techniques. Target and reference trajectory tracking are obtained by using results from standard linear quadratic regulator theory.

The result of this research is the creation of a framework for the development of an industrially implementable controller that improves the current technology. This framework provides a rigorous and flexible theoretical basis that retains, and in several cases enhances, the features necessary to handle chemical process control applications.

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University of Wisconsin
Department of Chemical Engineering
Madison WI 53706