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.
CITATION STYLE
Janakiraman, S., & Devanathan, R. (2019). Tuning linearization transformation using back-propagation algorithm. International Journal of Engineering and Advanced Technology, 9(1), 1471–1476. https://doi.org/10.35940/ijeat.A1261.109119
Mendeley helps you to discover research relevant for your work.