Switching ETM-based neural adaptive output feedback control for nonaffine stochastic MIMO nonlinear systems subject to deferred constraint

Xiaona Song, Peng Sun, Choon Ki Ahn, Shuai Song

Research output: Contribution to journalArticlepeer-review

Abstract

This article focuses on the neural adaptive output feedback control study related to nonaffine stochastic multiple-input, multiple-output nonlinear plants. First, a K-filter state observer based on a radial basis function neural network is designed to estimate the remaining unavailable states. Then, a novel adaptive command-filtered backstepping output feedback control framework is established, where an improved command filter with a fractional-order parameter is applied to conquer the calculation size problem. Specifically, the highlight of this work is that it designs a modified error compensation signal and incorporates the concept of deferred constraint to eradicate the negative effect caused by the filter errors. In addition, the network bandwidth resources, control impulse, and control accuracy are synthesized using an amended switching event-triggered mechanism. The theoretical analysis proved that the proposed control approach guarantees that the tracking error can converge to a preassigned region within a user-defined time while the violation of the deferred output constraint can be excluded. Two illustrative studies are provided to demonstrate the validity and superiority of the developed control method.

Original languageEnglish
Pages (from-to)668-679
Number of pages12
JournalNeural Networks
Volume167
DOIs
Publication statusPublished - 2023 Oct

Bibliographical note

Publisher Copyright:
© 2023 Elsevier Ltd

Keywords

  • Deferred constraint
  • Filter state observer
  • Fractional-order parameter
  • Nonaffine stochastic multiple-input and multiple-output (MIMO) nonlinear plants
  • Switching event-triggered mechanism

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence

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