Adaptive Neural Network-Based Observer Design for Switched Systems With Quantized Measurements

Liheng Chen, Yanzheng Zhu, Choon Ki Ahn

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


This study is concerned with the adaptive neural network (NN) observer design problem for continuous-time switched systems via quantized output signals. A novel NN observer is presented in which the adaptive laws are constructed using quantized measurements. Then, persistent dwell time (PDT) switching is considered in the observer design to describe fast and slow switching in a unified framework. Accurate estimations of state and actuator efficiency factor can be obtained by the proposed observer technique despite actuator degradation. Finally, a simulation example is provided to illustrate the effectiveness of the developed NN observer design approach.

Original languageEnglish
JournalIEEE Transactions on Neural Networks and Learning Systems
Publication statusAccepted/In press - 2021


  • Actuator degradation
  • Actuators
  • Artificial neural networks
  • Degradation
  • Observers
  • Quantization (signal)
  • Switched systems
  • Switches
  • adaptive neural network (NN) observer
  • persistent dwell time
  • signal quantization
  • switched systems.

ASJC Scopus subject areas

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


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