Model-based predictive control studies for a continuous pulpdigester
Wisnewski, P.A.; Doyle, F.J., III
Control Systems Technology, IEEE Transactions on
Volume 9, Issue 3, May 2001 Page(s):435 - 444
Digital Object Identifier 10.1109/87.918897
Summary:As various industries continue to develop complex, fundamental
process models, there exists a need to systematically incorporate these
complex models into the controller design. Three model predictive
controllers (MPG), each incorporating internal models with varying
degrees of complexity, are applied to a nonlinear, fundamental,
continuous pulp digester “plant.” The first two controllers
utilize linear models, one obtained through subspace identification and
the other obtained from the linearization of the fundamental model. The
third model predictive controller uses the complex, nonlinear digester
model with extended linearization to update the controller model for
future predictions and control computations. The two MPC controllers
based on the fundamental model, both linear and nonlinear, had better
closed-loop performance than the controller utilizing the subspace
identified model. The closed-loop performance of the linear and
nonlinear MPC controllers (based on the fundamental model) were
indistinguishable for stochastic disturbance rejection
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