Neural network approach to variable structure based adaptive tracking of SISO systems

Author

Fu, Li-Chen

Author_Institution

Dept. of Electr. Eng., Nat. Taiwan Univ., Taipei, Taiwan

fYear

1996

fDate

5-6 Dec 1996

Firstpage

148

Lastpage

153

Abstract

This paper presents a novel approach to adaptive tracking control of linear SISO systems, which can solve the traditional model reference adaptive control (MRAC) problems. In this approach, a neural network universal approximator is included to furnish an online estimate of a function of the state and some signals relevant to the desired trajectory. The salient feature of the present work is that a rigorous proof via Lyapunov stability theory is provided. It is shown that the output error will fall into a residual set which can be made arbitrarily small

Keywords

Lyapunov methods; function approximation; model reference adaptive control systems; neurocontrollers; state estimation; tracking; variable structure systems; Lyapunov stability theory; MRAC; VSS; linear SISO systems; model reference adaptive control; neural network universal approximator; output error; variable structure based adaptive tracking; Adaptive control; Adaptive systems; Computer science; Control systems; Electric variables control; Error correction; Neural networks; Nonhomogeneous media; Programmable control; Sliding mode control;

fLanguage

English

Publisher

ieee

Conference_Titel

Variable Structure Systems, 1996. VSS '96. Proceedings., 1996 IEEE International Workshop on

Conference_Location

Tokyo

Print_ISBN

0-7803-3718-2

Type

conf

DOI

10.1109/VSS.1996.578593

Filename

578593