Online black-box model identification and output prediction for sampled-data systems

Author

Zaheer, Asim ; Salman, Molly

Author_Institution

Coll. of Electr. & Mech. Eng., Nat. Univ. of Sci. & Technol., Islamabad, Pakistan

fYear

2014

fDate

22-25 Oct. 2014

Firstpage

1095

Lastpage

1100

Abstract

In this work, black-box model identification and output prediction for unknown sampled-data minimum phase system has been achieved. Feedforward neural network (multilayer perceptron) is used for system identification. Unscented Kalman Filter (UKF) online determine weights of neural network and predicts output in open-loop sampled-data configuration. Magnetic levitation and DC motor model has been identified in computer simulations using the presented black-box identification and prediction scheme.

Keywords

Kalman filters; identification; multilayer perceptrons; neurocontrollers; nonlinear filters; open loop systems; predictive control; sampled data systems; DC motor model; UKF; computer simulations; feedforward neural network; magnetic levitation; multilayer perceptron; online black-box model identification; open-loop sampled-data configuration; output prediction; prediction scheme; system identification; unknown sampled-data minimum phase system; unscented Kalman filter; Viscosity; DC motor; UKF; black-box; magnetic levitation system; minimum phase system; neural network;

fLanguage

English

Publisher

ieee

Conference_Titel

Control, Automation and Systems (ICCAS), 2014 14th International Conference on

Conference_Location

Seoul

ISSN

2093-7121

Print_ISBN

978-8-9932-1506-9

Type

conf

DOI

10.1109/ICCAS.2014.6987543

Filename

6987543