Infrared and Laser Engineering, Volume. 53, Issue 6, 20230667(2024)

Adaptive sliding mode control by memristor-based neural network and its application

Di LIN1,2, Yiming WU1,2, Sen YANG3, Yin ZHANG3, and Mingshu ZHAO3
Author Affiliations
  • 1Tongren Intelligent Technology (Xi’an) Co., Ltd, Xi’an Jiaotong University Tongren Intelligent Systems Science and Intelligent Device Physics Joint Research Institute, Xi’an 710115, China
  • 2Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China
  • 3School of Physics, Xi’an Jiaotong University, Xi’an 710115, China
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    Figures & Tables(12)
    Signal connection diagram of the entire servo system
    Servo control board hardware and memristor simulation
    Neural network sliding mode adaptive controller structure based on memristor
    Simulink model
    Simulation of basic neural network sliding mode control aigorithm
    Estimation of f(x) using basic neural networks
    Simulation of improved neural network adaptive sliding mode variable structure control algorithm
    Online estimation of f(x) using neural network adaptive sliding mode variable structure control algorithm
    Comparison of accuracy between conventional sliding mode control algorithms and neural network adaptive sliding mode control algorithms
    Optoelectronic pod experimental testing equipment,which is mounted on a swing platform to test stable accuracy scenarios
    The effect of optoelectronic pod tracking drones in an outfield testing environment
    Actual tracking error of unmanned aerial vehicle miss distance in photoelectric pod
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    Di LIN, Yiming WU, Sen YANG, Yin ZHANG, Mingshu ZHAO. Adaptive sliding mode control by memristor-based neural network and its application[J]. Infrared and Laser Engineering, 2024, 53(6): 20230667

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    Paper Information

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    Received: Dec. 1, 2023

    Accepted: --

    Published Online: Jul. 31, 2024

    The Author Email:

    DOI:10.3788/IRLA20230667

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