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 journalArticlepeer-review

56 Citations (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
Pages (from-to)351-365
Number of pages15
JournalNeurocomputing
Volume363
DOIs
Publication statusPublished - 2019 Oct 21

Bibliographical note

Funding Information:
This work was supported by the Fundamental Research Funds for the Central Universities under grant 31020180QD076 and the Natural Science Basic Research Plan in Shaanxi Province of China under grant 2019JQ-035 .

Publisher Copyright:
© 2019 Elsevier B.V.

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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