Quantized Decentralized Adaptive Neural Network PI Tracking Control for Uncertain Interconnected Nonlinear Systems with Dynamic Uncertainties

Haibin Sun, Guangdeng Zong, Choon Ki Ahn

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

In this paper, a decentralized adaptive neural network proportional-integral (PI) tracking control scheme is proposed for interconnected nonlinear systems with input quantization and dynamic uncertainties. This algorithm is underpinned by the use of the dynamic signal, graph theory, and function recombination to deal with the difficulties existing in the nontriangular form, unmodeled dynamics, and unknown interconnected terms. Recalling the backstepping method and neural network approximation technology, a new PI tracking controller characterized by simple structure and easy implementation is developed which ensures that all the closed-loop signals are uniformly ultimately bounded. The effectiveness of the obtained controller is exemplified via a numerical example and an application to an inverted pendulum.

Original languageEnglish
Article number8736513
Pages (from-to)3111-3124
Number of pages14
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume51
Issue number5
DOIs
Publication statusPublished - 2021 May

Keywords

  • Decentralized control
  • input quantization
  • interconnected system
  • neural network-based control
  • nontriangular form
  • proportional-integral (PI) tracking controller

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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