Prescribed performance fixed-time recurrent neural network control for uncertain nonlinear systems

Junkang Ni, Choon Ki Ahn, Ling Liu, Chongxin Liu

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

This paper investigates fixed-time prescribed performance control problem for uncertain strict-feedback nonlinear systems with unknown dead zone. First, a novel prescribed performance function (PPF) is proposed and a coordinate transformation is employed to transform the prescribed performance constrained system into an unconstrained one. Next, recurrent neural network is introduced to estimate the uncertain dynamics and fixed-time differentiator is utilized to obtain the derivative of virtual control. Then, a fixed-time dynamic surface control is developed to deal with dead zone and guarantee the convergence of the tracking error within a fixed time. Lyapunov stability analysis shows that the presented control scheme can achieve the fixed-time convergence of the error variables, while the other closed-loop system signals are bounded. Finally, numerical simulation validates the effectiveness of the presented control scheme.

Original languageEnglish
JournalNeurocomputing
DOIs
Publication statusPublished - 2019 Jan 1

Keywords

  • Dead zone
  • Fixed-time control
  • Prescribed performance control
  • Recurrent neural network control
  • Uncertain nonlinear system

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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