A feedback linearization technique by using neural networks: application to bioprocess control

  • Authors:

  • Y. S. Boutalis;O. I. Kosmidou

  • Affiliations:

  • Automatic Control Systems Laboratory, Department of Electrical & Computer Engineering, Democritus University of Thrace, GR-67100 XANTHI, Greece;Automatic Control Systems Laboratory, Department of Electrical & Computer Engineering, Democritus University of Thrace, GR-67100 XANTHI, Greece

  • Venue:
  • ICCMSE '03 Proceedings of the international conference on Computational methods in sciences and engineering
  • Year:
  • 2003

Quantified Score

Hi-index 0.00

Visualization

Abstract

Most physical systems operations are nonlinear in nature and hence they should be described by means of nonlinear mathematical models. Since nonlinear models are not convenient for control purposes, due to both theoretical and computational reasons, they are often linearized by using appropriate exact or approximate linearization techniques [5], [4]. Among them, feedback linearization [5] is the most theoretically rigorous method; it consists in finding a feedback control law and a state variable transformation (diffeomorphism) such that the closed loop system model becomes linear, in the new coordinate variables. However, feedback linearization requires some strong constraints to be satisfied by the original nonlinear system, and thus its applicability is quite restricted. If, in addition, the original system is characterized by uncertain parameters, external disturbances and unmeasured state variables, as is the case of bioprocess control systems, the linearization problem becomes particularly complex and almost inextricable.