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Neural network-based event-triggered data-driven control of disturbed nonlinear systems with quantized input.

Xianming Wang1, Hamid Reza Karimi2, Mouquan Shen3

  • 1School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing, 211816, China.

Neural Networks : the Official Journal of the International Neural Network Society
|October 21, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces event-triggered control for nonlinear systems with quantized inputs. A neural network estimates system states and disturbances, ensuring stability and reducing control signal updates.

Keywords:
Event-triggered controlModel-free adaptive controlNeural network

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Area of Science:

  • Control Systems Engineering
  • Nonlinear Dynamics
  • Artificial Intelligence

Background:

  • Event-triggered control reduces communication and computation load.
  • Quantized control introduces challenges in system stability and performance.
  • Data-driven approaches offer alternatives to model-based control.

Purpose of the Study:

  • To design an event-triggered, data-driven control strategy for disturbed nonlinear systems with quantized input.
  • To address quantization errors using a uniform quantizer with decreasing intervals.
  • To develop a neural network-based estimation for system dynamics and disturbances.

Main Methods:

  • Utilizing a uniform quantizer with decreasing quantization intervals to minimize error.
  • Employing a neural network-based estimation strategy for pseudo partial derivatives and disturbances.
  • Developing an input triggering rule based on estimated disturbances, quantization error, and tracking errors.
  • Applying Lyapunov stability analysis to ensure uniform ultimate boundedness of the error system.

Main Results:

  • A novel event-triggered data-driven control scheme for quantized nonlinear systems.
  • Effective estimation of unknown disturbances and system states using neural networks.
  • Demonstrated stability of the closed-loop system through Lyapunov analysis.
  • Validation of the proposed control strategy via simulation.

Conclusions:

  • The proposed event-triggered control scheme effectively manages quantized inputs in disturbed nonlinear systems.
  • The neural network-based estimation significantly improves disturbance rejection and system performance.
  • The method provides a robust and efficient approach for practical control applications.