Neural network based direct adaptive control for a class of affine nonlinear systems

Author

Kar, Indrani ; Behera, Laxmidhar

Author_Institution

Dept. of Electr. Eng., Indian Inst. of Technol., Kanpur

fYear

2006

fDate

4-6 Oct. 2006

Firstpage

2030

Lastpage

2035

Abstract

This paper presents a neural network based direct adaptive control scheme for a class of affine nonlinear systems which are exactly input-output linearizable by nonlinear state feedback. When the system dynamics are completely unknown, the control input comprises two terms. One is an adaptive feedback linearization term and the other one is a sliding mode term. The neural networks weight update laws have been derived to make the closed loop system Lyapunov stable. It is shown that the proposed control action can also be applied to multi-input-multi-output systems with minor modifications. Simulation results are presented to validate the theoretical formulations

Keywords

Lyapunov methods; adaptive control; closed loop systems; linearisation techniques; neurocontrollers; nonlinear control systems; state feedback; variable structure systems; Lyapunov stability; adaptive feedback linearization; affine nonlinear system; closed loop system; direct adaptive control; input-output linearizable; multiinput-multioutput systems; neural network; nonlinear state feedback; sliding mode term; system dynamics; Adaptive control; Closed loop systems; Control systems; Neural networks; Neurofeedback; Nonlinear dynamical systems; Nonlinear systems; Sliding mode control; State feedback; Two-term control;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control, 2006 IEEE

Conference_Location

Munich

Print_ISBN

0-7803-9797-5

Electronic_ISBN

0-7803-9797-5

Type

conf

DOI

10.1109/CACSD-CCA-ISIC.2006.4776952

Filename

4776952