Intelligent modeling, observation, and control for nonlinear systems

Author

Schröder, Dierk ; Hintz, Christian ; Rau, Martin

Author_Institution

Inst. for Electr. Drives, Tech. Univ. Munchen, Germany

Volume

6

Issue

2

fYear

2001

fDate

6/1/2001 12:00:00 AM

Firstpage

122

Lastpage

131

Abstract

We present identification methods for nonlinear mechatronic systems. First, we consider a system consisting of a known linear part and an unknown static nonlinearity. With this approach, using an intelligent observer, it is possible to identify the nonlinear characteristic and to estimate all unmeasurable system states. The identification result of the nonlinearity and the estimated system states are used to improve the controller performance. Secondly, the first approach is extended to systems where both the linear parameters and the nonlinear characteristic are unknown. This is achieved by implementing the intelligent observer as a structured recurrent neural network

Keywords

drives; intelligent control; mechatronics; nonlinear control systems; observers; recurrent neural nets; uncertain systems; intelligent modeling; intelligent observation; nonlinear characteristic; nonlinear mechatronic systems; structured recurrent neural network; unknown static nonlinearity; unmeasurable system states; Adaptive control; Control nonlinearities; Control system synthesis; Intelligent structures; Nonlinear control systems; Nonlinear systems; Observers; Programmable control; Recurrent neural networks; State estimation;

fLanguage

English

Journal_Title

Mechatronics, IEEE/ASME Transactions on

Publisher

ieee

ISSN

1083-4435

Type

jour

DOI

10.1109/3516.928725

Filename

928725