Neural identification and indirect control of a nonlinear mechanical oscilatory plant

Author

Baruch, Ieroham S. ; Hernandez, Sergio M.

Author_Institution

Dept. of Autom. Control, CINVESTAV-IPN, Mexico City, Mexico

fYear

2012

fDate

6-8 Sept. 2012

Firstpage

244

Lastpage

249

Abstract

A new Modular Recurrent Trainable Neural Network (MRTNN) has been used for system identification of nonlinear oscillatory mechanical plant. The first MRTNN module identified the exponential part of the unknown plant and the second one - the oscillatory part of the plant. The plant has been controlled by an adaptive sliding mode control system with integral term. The RTNN controller used the estimated parameters and states to suppress the plant oscillations and the static plant output control error is reduced by an I-term added to the control.

Keywords

adaptive control; identification; industrial control; neurocontrollers; nonlinear control systems; oscillations; parameter estimation; recurrent neural nets; variable structure systems; I-term; MRTNN module; RTNN controller; adaptive sliding mode control system; indirect control; modular recurrent trainable neural network; neural identification; nonlinear mechanical oscillatory plant; parameter estimation; plant control; plant oscillations; static plant output control error; system identification; Equations; Mathematical model; Oscillators; Recurrent neural networks; System identification; Topology; Vectors; indirect sliding mode control with integral term; modular recurrent neural network; nonlinear oscillatory plant;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems (IS), 2012 6th IEEE International Conference

Conference_Location

Sofia

Print_ISBN

978-1-4673-2276-8

Type

conf

DOI

10.1109/IS.2012.6335143

Filename

6335143