Adaptive Neural Asymptotic Tracking of Uncertain Non-Strict Feedback Systems With Full-State Constraints via Command Filtered Technique

Chun Xin, Yuan Xin Li, Choon Ki Ahn

Research output: Contribution to journalArticlepeer-review

Abstract

This brief addresses the adaptive neural asymptotic tracking issue for uncertain non-strict feedback systems subject to full-state constraints. By introducing the significant nonlinear transformed function (NTF), the command filtered technology, and the boundary estimation method into control design, a novel command filtered backstepping adaptive controller is proposed. The proposed control scheme is able to not only deal with full-state constraints but also avoid the ``explosion of complexity'' issue. By means of a Lyapunov stability analysis, we prove that: 1) the tracking error asymptotically converges to zero; 2) all the variables in the controlled systems are bounded; and 3) all the states are constrained in the asymmetric predefined sets. Finally, a numerical simulation is used to demonstrate the validity of the proposed algorithm.

Original languageEnglish
JournalIEEE Transactions on Neural Networks and Learning Systems
DOIs
Publication statusAccepted/In press - 2022

Keywords

  • Artificial neural networks
  • Asymptotic tracking control
  • Backstepping
  • command filter backstepping
  • Explosions
  • full-state constraints
  • Lyapunov methods
  • neural networks (NNs)
  • non-strict feedback systems.
  • Nonlinear systems
  • Safety
  • Uncertainty

ASJC Scopus subject areas

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

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