Nonlinear system control using neural networks based on trajectory linearization

Author

Liu, Yong ; Huang, Rui ; Zhu, Jim

Author_Institution

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

Volume

1

fYear

2004

fDate

2-4 Sept. 2004

Firstpage

806

Abstract

A nonlinear control technique using neural networks based on trajectory linearization control (TLC) is proposed. Trajectory linearization control is a novel control design method that combines a dynamic inversion and linear time-varying feedback stabilization along a nominal trajectory. In this paper, the TLC design procedure using neural networks model is developed, and the stability and robustness are analyzed. Simulation results are presented to show the feasibility of the proposed method.

Keywords

control system synthesis; feedback; linear systems; linearisation techniques; neurocontrollers; nonlinear control systems; position control; robust control; time-varying systems; dynamic inversion; linear time varying feedback stabilization; neural network model; nonlinear control system technique; robustness; trajectory linearization control design method; Control design; Control systems; Linear feedback control systems; Neural networks; Neurofeedback; Nonlinear control systems; Nonlinear dynamical systems; Nonlinear systems; Robust stability; Stability analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Applications, 2004. Proceedings of the 2004 IEEE International Conference on

Print_ISBN

0-7803-8633-7

Type

conf

DOI

10.1109/CCA.2004.1387313

Filename

1387313