F. LI387
5. Conclusions
A dynamic neural network based online nonlinear identi-
fier for a binary distillation column is designed. The
learning algorithm of the network weights is established
in detail, which can guarantee the boundedness of the
identification error. To deal with the modeling error, a
nonlinear H∞ controller based on the identifier is given
by choosing the penalty variables for the column. The
effectiveness of the proposed strategy is demonstrated in
simulation results. The algorithm developed in this paper
can be applied to other chemical processes as well.
6. Acknowledgements
The author thanks the reviewers for very helpful com-
ments. Views expressed in this paper are the author’s
professional opinions and do not necessarily represent
the official position of the US Food and Drug Admini-
stration.
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