Journal
JOURNAL OF PROCESS CONTROL
Volume 13, Issue 8, Pages 819-830Publisher
ELSEVIER SCI LTD
DOI: 10.1016/S0959-1524(03)00027-1
Keywords
feedback linearization; hybrid neural control; Gray-box modeling; first principles knowledge
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In this paper, discrete-time control schemes based on feedback linearization of serial gray-box models are considered for partially known nonlinear processes. These techniques combine the benefits of feedback linearization, neural networks, and serial gray-box modeling, which result in larger dynamic operating ranges, better extrapolation properties, and fewer data acquisition efforts in comparison with the corresponding black-box-based schemes. First-principles-based serial gray-box models are classified into invertible and non-invertible structures for training purposes, and an improved approximate feedback linearization scheme based on Taylor series terms of a non-affine gray-box model is proposed. Moreover, an affine gray-box model is developed for applying the exact feedback linearization scheme. Simulation results on a fermentation process show that the proposed methods yield significant improvement in modeling and control performance in comparison with that of the black-box feedback linearization schemes. (C) 2003 Elsevier Ltd. All rights reserved.
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