Neural Network-Based Sampled-Data Control for Switched Uncertain Nonlinear Systems

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

Research output: Contribution to journalArticlepeer-review

40 Citations (Scopus)


This article investigates the sampled-data stabilization problem of a class of switched nonlinear systems. All subsystems of the considered system are allowed to be unstabilizable. To relax the restrictions on unknown nonlinear functions in some existing results, we use the nonlinear approximation ability of radial basis function neural networks. Novel mode-dependent adaptive laws and sampled-data control laws are constructed by only using the system states' information at sampling instants. A novel sampled-data switching condition is derived, which can avoid Zeno behavior effectively. To guarantee that all states of the closed-loop system (CLS) are bounded, a new allowable sampling period is deduced. Finally, we demonstrate the proposed method's effectiveness through two examples.

Original languageEnglish
Article number8924765
Pages (from-to)5437-5445
Number of pages9
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Issue number9
Publication statusPublished - 2021 Sept


  • Adaptive neural network (NN) control
  • nonlinear systems
  • sampled-data control
  • state feedback
  • switched systems

ASJC Scopus subject areas

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


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