Design and Analysis of FTZNN Applied to the Real-Time Solution of a Nonstationary Lyapunov Equation and Tracking Control of a Wheeled Mobile Manipulator

Lin Xiao, Bolin Liao, Shuai Li, Zhijun Zhang, Lei Ding, Long Jin

Research output: Journal article publicationJournal articleAcademic researchpeer-review

122 Citations (Scopus)

Abstract

The Lyapunov equation is widely employed in the engineering field to analyze stability of dynamic systems. In this paper, based on a new evolution formula, a novel finite-time recurrent neural network (termed finite-time Zhang neural network, FTZNN) is proposed and studied for solving a nonstationary Lyapunov equation. In comparison with the original Zhang neural network (ZNN) model for a nonstationary Lyapunov equation, the convergence performance has a remarkable improvement for the proposed FTZNN model and can be accelerated to finite time. Besides, by solving the differential inequality, the time upper bound of the FTZNN model is computed theoretically and analytically. Simulations are conducted and compared to validate the superiority of the FTZNN model to the original ZNN model for solving the nonstationary Lyapunov equation. At last, the FTZNN model is successfully applied to online tracking control of a wheeled mobile manipulator.

Original languageEnglish
Article number7953678
Pages (from-to)98-105
Number of pages8
JournalIEEE Transactions on Industrial Informatics
Volume14
Issue number1
DOIs
Publication statusPublished - 1 Jan 2018

Keywords

  • Finite-time convergence
  • nonstationary Lyapunov equation
  • tracking control
  • upper bound
  • wheeled mobile manipulator
  • Zhang neural network (ZNN)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering

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