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This paper studies the design of feedback controllers for trajectory tracking in single-inputlsingle-output nonlinear systems X = f(x) + g(x) u, y = h(x). A nonlinear transformation of the form z, = K ( X ) + X(x) u that transforms this nonlinear input/output system into a linear system is first constructed. On the basis of this transformation, an approach for designing control laws for trajectory tracking4s presented. The control law is robust in the sense that small changes in it do not produce large steady state errors or loss of stability. The theory provides a unified framework for treating control problems arising in nonlinear chemical processes; this is illustrated by a batch reactor control example.

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This article presents the application of three black-box modeling methods to two industrial polymerization processes to predict the melt index, which is considered an important quality variable determining product specifications. The modeling methods covered in this study are support vector machines (SVMs; known as state-of-the-art modeling methods), partial least squares (PLS), and artificial neural networks (ANNs); the processes are styrene-acrylonitrile (SAN) and polypropylene (PP) polymerizations currently operated for commercial purposes in Korea. Brief outlines of the modeling procedure are presented for each method, followed by the procedures for training and validating the models. The SVM models yield the best prediction performances for both the SAN and PP polymerization processes. However, the ANN models fail to accurately predict the melt index when sufficient data are not available for model training in the PP polymerization process. The PLS models are not effective either when applied to the SAN polymerization process, for which the melt index has strong nonlinear functionality with the process variables. The good prediction performance that the SVM models show despite the insufficient data or strong process nonlinearity suggests that SVMs can be effectively used as alternative to PLS or ANNs for modeling the melt indices in other polymerization processes as well.