James B. Rawlings Research Group


John W. Eaton
B.S. Ch.E., Oregon State University, 1985
Ph.D., University of Texas at Austin, 1992
jwe[AT]bevo[DOT]che[DOT]wisc[DOT]edu
[John W. Eaton]

Current Work

Octave is an interactive language for numerical computing that is mostly compatible with MATLAB. Originally intended to be companion software for an undergraduate-level textbook on chemical reactor design being written by James B. Rawlings and John G. Ekerdt at the University of Texas, it has become much more than just another courseware package with limited utility beyond the classroom. It is currently in use by thousands of people at educational, commercial, and government sites worldwide.

The Octave interpreter is written in a mixture of C and C++, but most of the numerical methods are handled by standard Fortran libraries such as the BLAS, LAPACK, MINPACK, QUADPACK, ODEPACK, and DASSL. To smoothly interface with the interpreter, the numerical libraries have been packaged in a library of C++ classes.

Though Octave is compatible with MATLAB in many ways, it is not intended to be a clone. Octave adds many interesting new features and extends the language in fundamentally new ways. Because Octave is available in source form, anyone can experiment with adding new features or modifying the language.

In a relatively short period of time, Octave has become a quite capable system for solving many numerical problems, but it is still far from complete. Some long-term goals include adding a programmable graphical user interface, improving the overall efficiency of the language, and automatic generation of C++ code.

Everyone is encouraged to share Octave with others under the terms of the GNU General Public License (GPL) as published by the Free Software Foundation (FSF). The complete source code for Octave and more information about this project is available on the web at www.octave.org.

Publications

1
John W. Eaton and James B. Rawlings.
Ten years of Octave -- recent developments and plans for the future.
In Kurt Hornik and Fritz Leisch, editors, Proceedings of the 3rd International Workshop on Distributed Statistical Computing, Vienna, Austria, March 2003.

2
John W. Eaton.
GNU Octave Manual.
Network Theory Limited, 2002.

3
James B. Rawlings, Babatunde A. Ogunnaike, and John W. Eaton, editors.
Chemical Process Control CPC VI, Austin, TX, 2002. CACHE.

4
John W. Eaton.
Octave: Past, present and future.
In Kurt Hornik and Fritz Leisch, editors, Proceedings of the 2nd International Workshop on Distributed Statistical Computing, March 15-17, 2001, Technische Universität Wien, Vienna, Austria, 2001.
ISSN 1609-395X.

5
James B. Rawlings and John W. Eaton, editors.
Chemical Engineering Faculty Directory (1995-1996), volume 44.
AIChE, New York, 1995.

6
John W. Eaton and James B. Rawlings.
Octave--a high level interactive language for numerical computations.
CACHE News, 40:11-18, Spring 1995.

7
Edward S. Meadows, Michael A. Henson, John W. Eaton, and James B. Rawlings.
Receding horizon control and discontinuous state feedback stabilization.
Int. J. Control, 62(5):1217-1229, 1995.

8
John W. Eaton, James B. Rawlings, and Lyle H. Ungar.
Stability of neural net based model predictive control.
In Proceedings of the 1994 American Control Conference, pages 2481-2485, 1994.

9
John W. Eaton and James B. Rawlings.
Model predictive control of chemical processes.
Chem. Eng. Sci., 47(4):705-720, 1992.

10
John W. Eaton.
Finite Horizon Predictive Control of Chemical Processes.
PhD thesis, The University of Texas at Austin, March 1992.

11
James B. Rawlings, Walter R. Witkowski, and John W. Eaton.
Modelling and control of crystallizers.
Powder Tech., 69(1):3-9, 1992.

12
John W. Eaton and James B. Rawlings.
Model predictive control of chemical processes.
In Proceedings of the 1991 American Control Conference, pages 1790-1795, 1991.

13
John W. Eaton and James B. Rawlings.
Feedback control of chemical processes using on-line optimization techniques.
Comput. Chem. Eng., 14(4/5):469-479, 1990.

14
James B. Rawlings and John W. Eaton.
Optimal control and model identification applied to the Shell standard control problem.
In David M. Prett, Carlos E. Garcí, and Brian L. Ramaker, editors, The Second Shell Process Control Workshop, pages 209-240. Butterworths, 1990.

15
John W. Eaton and James B. Rawlings.
Feedback control of chemical processes using on-line optimization techniques.
Annual AIChE Meeting, Washington, D.C., November 1988.

16
James B. Rawlings, Walter R. Witkowski, and John W. Eaton.
Control issues arising in population balance models.
In Proceedings of the 1989 American Control Conference, pages 677-682, 1989.

17
John W. Eaton, James B. Rawlings, and Thomas F. Edgar.
Model-predictive control and sensitivity analysis for constrained nonlinear processes.
In T. J. McAvoy, Y. Arkun, and E. Zafiriou, editors, Proceedings of the 1988 IFAC Workshop on Model Based Process Control, pages 129-135, Oxford, 1989. Pergamon Press.

Thesis Abstract

Finite Horizon Predictive Control of Chemical Processes

This dissertation presents a strategy for model-predictive control of processes modelled by nonlinear differential-algebraic equations. This strategy makes use of a repeated optimization of an open-loop performance objective over a finite time horizon. The manipulated variable profile determined from the solution of the open-loop optimization is implemented until a plant measurement becomes available.

Two nonlinear programming approaches for solving the open-loop optimal control problem are examined in detail, and example problems are solved using both methods. The first approach eliminates the dynamic constraints by solving them as a subproblem. The second second approach uses orthogonal collocation on finite elements to convert the differential equations to algebraic constraints that are solved directly by standard nonlinear programming software.

Feedback is incorporated into the algorithm by using the measurement to update the optimization problem for the next time step. Several methods for updating the optimization problem are discussed, including resetting the model's states to measured or estimated values, estimating model parameters, or inferring output disturbances.

Personal Web Page: jbrwww.che.wisc.edu/~jwe

[ Home | People | Projects | Recent Presentations | Publications | Tech Reports | Contact Us ]

University of Wisconsin
Department of Chemical Engineering
Madison WI 53706