Latin Abstract :
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.
NaturalLanguageKeyword :
Hybrid neural control , Gray-box modeling , First principles knowledge , Feedback Linearization
