Neural-Based Decentralized Adaptive Finite-Time Control for Nonlinear Large-Scale Systems with Time-Varying Output Constraints

Peihao Du, Hongjing Liang, Shiyi Zhao, Choon Ki Ahn

Research output: Contribution to journalArticlepeer-review

88 Citations (Scopus)

Abstract

This paper addresses the adaptive finite-time decentralized control problem for time-varying output-constrained nonlinear large-scale systems preceded by input saturation. The intermediate control functions designed are approximated by neural networks. Time-varying barrier Lyapunov functions are used to ensure that the system output constraints are never breached. An adaptive finite-time decentralized control scheme is devised by combining the backstepping approach with Lyapunov function theory. Under the action of the proposed approach, the system stability and desired control performance can be obtained in finite time. The feasibility of this control strategy is demonstrated by using simulation results.

Original languageEnglish
Article number8736293
Pages (from-to)3136-3147
Number of pages12
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume51
Issue number5
DOIs
Publication statusPublished - 2021 May

Keywords

  • Finite time
  • input saturation
  • neural network (NN)
  • nonlinear large-scale systems
  • time-varying output constraints

ASJC Scopus subject areas

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

Fingerprint

Dive into the research topics of 'Neural-Based Decentralized Adaptive Finite-Time Control for Nonlinear Large-Scale Systems with Time-Varying Output Constraints'. Together they form a unique fingerprint.

Cite this