Neural networks for tracking of unknown SISO discrete-time nonlinear dynamic systems

Muhammad Saleheen Aftab, Muhammad Shafiq*

*Corresponding author for this work

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

This article presents a Lyapunov function based neural network tracking (LNT) strategy for single-input, single-output (SISO) discrete-time nonlinear dynamic systems. The proposed LNT architecture is composed of two feedforward neural networks operating as controller and estimator. A Lyapunov function based back propagation learning algorithm is used for online adjustment of the controller and estimator parameters. The controller and estimator error convergence and closed-loop system stability analysis is performed by Lyapunov stability theory. Moreover, two simulation examples and one real-time experiment are investigated as case studies. The achieved results successfully validate the controller performance.

Original languageEnglish
Pages (from-to)363-374
Number of pages12
JournalISA Transactions
Volume59
DOIs
Publication statusPublished - Nov 1 2015

Keywords

  • Decentralized control
  • Direct adaptive inverse control
  • Indirect adaptive Inverse control
  • Lyapunov function Neural tracking
  • Stable adaptive tracking

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Applied Mathematics
  • Instrumentation

Fingerprint Dive into the research topics of 'Neural networks for tracking of unknown SISO discrete-time nonlinear dynamic systems'. Together they form a unique fingerprint.

  • Cite this