Physical-Model Based Control: Experiments with a Stirred-Tank Heater

In the pursuit of generic methods, control theory has become separated from its prime objective: the control of physical systems. These generic techniques have a wide range of applications yet do not easily allow inclusion of system specific information into the control design. There are two important categories for which the inclusion of system-specific information is important: partially-known systems and non-linear systems. Physical-Model Based Control (PMBC) is a novel approach to using such system-specific information. The objective of this paper is to demonstrate the experimental application of PMBC to a partiallyknown nonlinear system. In so doing, the performance of the PMBC method is evaluated, and it is demonstrated how process and control engineering insights can be combined within this PMBC framework to yield a novel system-specific control algorithm.

[1]  S. Liberty,et al.  Linear Systems , 2010, Scientific Parallel Computing.

[2]  D. Anderson,et al.  Algorithms for minimization without derivatives , 1974 .

[3]  P. J. Gawthrop,et al.  Improved control using dynamic process models : Process operations and control , 1996 .

[4]  S. Żak,et al.  Comparative study of non-linear state-observation techniques , 1987 .

[5]  B. Mark On Self Tuning Regulators , 1972 .

[6]  G. R. Sullivan,et al.  Generic model control (GMC) , 1988 .

[7]  Karl Johan Åström,et al.  Self-Tuning Controllers Based on Pole-Zero Placement , 1980 .

[8]  Pieter Eykhoff Every good regulator of a system must be a model of that system , 1994 .

[9]  P. Gawthrop,et al.  Bond graph based control , 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.

[10]  Alan S. Perelson,et al.  System Dynamics: A Unified Approach , 1976, IEEE Transactions on Systems, Man, and Cybernetics.

[11]  Peter J. Gawthrop,et al.  Continuous-time self-tuning control , 1987 .

[12]  Huibert Kwakernaak,et al.  Linear Optimal Control Systems , 1972 .

[13]  Gene F. Franklin,et al.  Feedback Control of Dynamic Systems , 1986 .

[14]  W. Ashby,et al.  Every Good Regulator of a System Must Be a Model of That System , 1970 .

[15]  O. Jacobs,et al.  Introduction to Control Theory , 1976, IEEE Transactions on Systems, Man, and Cybernetics.

[16]  P. Gawthrop Physical model-based control: A bond graph approach , 1995 .

[17]  David E. Hardt,et al.  Controller Design in the Physical Domain , 1989, 1989 American Control Conference.

[18]  Peter J. Gawthrop,et al.  BOND-GRAPH BASED ADAPTIVE CONTROL , 1993 .

[19]  Arthur Gelb,et al.  Applied Optimal Estimation , 1974 .

[20]  Björn Wittenmark,et al.  On Self Tuning Regulators , 1973 .

[21]  R. W. Jones,et al.  Bond Graph based Control: A Process Engineering Example , 1992, 1992 American Control Conference.

[22]  L. Hunt,et al.  Observers for nonlinear systems in steady state , 1994, IEEE Trans. Autom. Control..