Open AccessTuning Linearization Transformation using Back-Propagation Algorithm
Author(s) -
S. Janakiraman,
R. Devanathan
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.a1261.109119
Subject(s) - linearization , control theory (sociology) , residual , nonlinear system , canonical form , quadratic equation , transformation (genetics) , computer science , feedback linearization , mathematics , algorithm , control (management) , physics , artificial intelligence , biochemistry , chemistry , geometry , quantum mechanics , pure mathematics , gene
The objective of linearization of a nonlinear system is to ensure smooth control of the linearized system through well-proven linear control methods. However, residual nonlinearities may still be present in a system after linearization either by design or due to mismatch between the system model and the actual plant. If the residual nonlinearities are not very significant, one can attempt to remove these by tuning the linearizing transformation by comparing the system to a linear canonical form. In this paper, we show how quadratic linearizing transformations of a three-phase horizontal gravity separator (TPS) model derived in an earlier paper by the authors can be tuned as in a neural network using error back-propagation by comparing it to a canonical linear model thus removing the nonlinearities within the tuning error limit.