Saturated Finite Interval Iterative Learning for Tracking of Dynamic Systems with HNN-Structural Output

Wenjun Xiong, Daniel W.C. Ho, Xinghuo Yu

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

27 Citations (Scopus)

Abstract

This brief investigates the interval iterative learning problem for dynamic systems with hierarchical neural network (HNN)-structural output. The first objective is to design the output of a dynamic system with HNN structure. A sufficient condition is obtained to achieve the interval tracking in a finite interval by applying iterative learning control (ILC). Then, the saturated ILC is considered into the discussed system, and a less conservative criterion is obtained to achieve the tracking in a finite interval using a network structure decomposition technique. Finally, simulation results are given to illustrate the usefulness of the developed criteria.
Original languageEnglish
Article number7160765
Pages (from-to)1578-1584
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume27
Issue number7
Online published16 Jul 2015
DOIs
Publication statusPublished - 1 Jul 2016

Research Keywords

  • Hierarchical neural network (HNN) output
  • interval iterative learning
  • saturated iterative learning
  • Uncertain nonlinearities.

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