Adaptive neural output-feedback control for a class of nonlower triangular nonlinear systems with unmodeled dynamics

Huanqing Wang, Peter Xiaoping Liu, Shuai Li, Ding Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

110 Citations (Scopus)

Abstract

This paper presents the development of an adaptive neural controller for a class of nonlinear systems with unmodeled dynamics and immeasurable states. An observer is designed to estimate system states. The structure consistency of virtual control signals and the variable partition technique are combined to overcome the difficulties appearing in a nonlower triangular form. An adaptive neural output-feedback controller is developed based on the backstepping technique and the universal approximation property of the radial basis function (RBF) neural networks. By using the Lyapunov stability analysis, the semiglobally and uniformly ultimate boundedness of all signals within the closed-loop system is guaranteed. The simulation results show that the controlled system converges quickly, and all the signals are bounded. This paper is novel at least in the two aspects: 1) an output-feedback control strategy is developed for a class of nonlower triangular nonlinear systems with unmodeled dynamics and 2) the nonlinear disturbances and their bounds are the functions of all states, which is in a more general form than existing results.

Original languageEnglish
Pages (from-to)3658-3668
Number of pages11
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume29
Issue number8
DOIs
Publication statusPublished - 1 Aug 2018

Keywords

  • Adaptive neural control
  • backstepping
  • nonlower triangular nonlinear systems
  • output-feedback control

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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