Adaptive Neural Network Control Based on Trajectory Linearization Control

Author

Liu, Yong ; Huang, Rui ; Zhu, Jim

Author_Institution

Sch. of Electr. Eng. & Comput. Sci., Ohio Univ., Athens, OH

Volume

1

fYear

0

fDate

0-0 0

Firstpage

417

Lastpage

421

Abstract

In this paper, an adaptive neural network nonlinear control method is developed based on trajectory linearization control (TLC). The adaptive neural network TLC control (ANNTLC) compensates the model nonlinear uncertainty adaptively, and improves controller performance. ANNTLC can also be used to simplify the TLC control design procedure by using a simplified model. A stable neural network learning rule is developed. The simulation result shows the feasibility of the proposed method

Keywords

adaptive control; compensation; control system synthesis; feedback; learning (artificial intelligence); linearisation techniques; neurocontrollers; nonlinear control systems; stability; time-varying systems; uncertain systems; adaptive neural network control; compensation; control design; neural network learning rule; nonlinear control; nonlinear uncertainty; stability; trajectory linearization control; Adaptive control; Adaptive systems; Control systems; Linear feedback control systems; Neural networks; Nonlinear control systems; Nonlinear systems; Open loop systems; Programmable control; Vehicle dynamics; Adaptive Control; Neural Network Control; Trajectory Linearization Control;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1712350

Filename

1712350