Neural-Network Approximation-Based Adaptive Periodic Event-Triggered Output-Feedback Control of Switched Nonlinear Systems

Shi Li, Choon Ki Ahn, Jian Guo, Zhengrong Xiang

Research output: Contribution to journalArticlepeer-review

67 Citations (Scopus)


This study considers an adaptive neural-network (NN) periodic event-triggered control (PETC) problem for switched nonlinear systems (SNSs). In the system, only the system output is available at sampling instants. A novel adaptive law and a state observer are constructed by using only the sampled system output. A new output-feedback adaptive NN PETC strategy is developed to reduce the usage of communication resources; it includes a controller that only uses event-sampling information and an event-triggering mechanism (ETM) that is only intermittently monitored at sampling instants. The proposed adaptive NN PETC strategy does not need restrictions on nonlinear functions reported in some previous studies. It is proven that all states of the closed-loop system (CLS) are semiglobally uniformly ultimately bounded (SGUUB) under arbitrary switchings by choosing an allowable sampling period. Finally, the proposed scheme is applied to a continuous stirred tank reactor (CSTR) system and a numerical example to verify its effectiveness.

Original languageEnglish
Article number9210563
Pages (from-to)4011-4020
Number of pages10
JournalIEEE Transactions on Cybernetics
Issue number8
Publication statusPublished - 2021 Aug


  • Adaptive neural-network (NN) control
  • output feedback
  • periodic event-triggered control (PETC)
  • switched nonlinear system (SNS)

ASJC Scopus subject areas

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


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