A robust nonlinear system identification algorithm using orthogonal polynomial network

Puneet Singla, Troy Henderson, John L. Junkins, John E. Hurtado

Research output: Contribution to journalConference articlepeer-review

5 Scopus citations

Abstract

A robust system identification algorithm is presented which makes use of linear system identification algorithms, such as Eigensystem Realization Algorithm, Observer/Kalman Identification, etc, and an orthogonal polynomial-based artificial neural network. Adaptive learning laws are derived by a thorough Lyapunov analysis to adjust different parameters of the neural network based model. The learning algorithm proposed in this paper is inspired by recent developments in adaptive control. The algorithm presented here is validated by analysis and simulation of examples based mainly on space applications. A detailed comparative study is performed to show the performance of the proposed algorithm with respect to some existing identification algorithms, specifically the Eigensystem Realization Algorithm.

Original languageEnglish (US)
Article numberAAS 05-163
Pages (from-to)983-1002
Number of pages20
JournalAdvances in the Astronautical Sciences
Volume120
Issue numberI
StatePublished - 2005
EventAAS/AIAA Space Flight Mechaics Meeting - Spaceflight Mechanics 2005 - Copper Mountain, CO, United States
Duration: Jan 23 2005Jan 27 2005

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Space and Planetary Science

Fingerprint Dive into the research topics of 'A robust nonlinear system identification algorithm using orthogonal polynomial network'. Together they form a unique fingerprint.

Cite this