Adaptive Backstepping control for MAPK cascade models using RBF neural networks

Author

Vamvoudakis, Kyriakos G. ; Christodoulou, Manolis A.

Author_Institution

Tech. Univ. of Crete, Chania

fYear

2007

fDate

27-29 June 2007

Firstpage

1

Lastpage

6

Abstract

In this paper, an Adaptive Backstepping Neural Network control approach is used for a class of affine nonlinear systems which describe the Mitogen Activated Protein Kinase (MAPK) cascade models in the strict feedback form. We consider some of forms of the MAPK cascade [4]. The close loop signals are semiglobally uniformly ultimately bounded and the output of the system is proven to follow a desired trajectory. Simulation results are presented to show the effectiveness of the approach proposed in order to control the MAPK output.

Keywords

adaptive control; biochemistry; biocontrol; feedback; neurocontrollers; nonlinear control systems; proteins; radial basis function networks; adaptive backstepping neural network control; feedback form; mitogen activated protein kinase cascade model; nonlinear system; radial basis function; Adaptive control; Adaptive systems; Backstepping; Biological system modeling; Differential equations; Neural networks; Nonlinear control systems; Nonlinear systems; Programmable control; Proteins;

fLanguage

English

Publisher

ieee

Conference_Titel

Control & Automation, 2007. MED '07. Mediterranean Conference on

Conference_Location

Athens

Print_ISBN

978-1-4244-1282-2

Electronic_ISBN

978-1-4244-1282-2

Type

conf

DOI

10.1109/MED.2007.4433707

Filename

4433707