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Self-triggered adaptive dynamic programming based on experience-replay and spectral adaptive law.

Yuteng Tian1, Xuemei Ren1, Yongfeng Lv2

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|December 16, 2025
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Summary

This study introduces a self-triggered adaptive dynamic programming (ADP) framework using experience replay (ER) and a spectral adaptive law (SPAL) for efficient, robust optimal control of unknown nonlinear systems, reducing communication needs.

Keywords:
Adaptive dynamic programmingExperience-replayOptimal controlSelf-triggered controlSpectral adaptive law

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Area of Science:

  • Control Systems Engineering
  • Artificial Intelligence
  • Machine Learning

Background:

  • Optimal control of unknown nonlinear systems presents significant challenges.
  • Existing adaptive dynamic programming (ADP) methods often require continuous monitoring and struggle with generalization.
  • Event-triggered approaches necessitate constant state observation for control triggering.

Purpose of the Study:

  • To propose a novel self-triggered adaptive dynamic programming (ADP) framework.
  • To enhance the robustness and efficiency of ADP for unknown nonlinear systems.
  • To reduce communication overhead in control systems through a self-triggered mechanism.

Main Methods:

  • Integration of experience replay (ER) and a spectral adaptive law (SPAL) within the ADP framework.
  • Development of a new critic neural network (NN) weight updating law based on SPAL for improved generalization.
  • Implementation of a self-triggered mechanism to predict the next triggering moment, avoiding continuous state monitoring.

Main Results:

  • The proposed SPAL-based NN weight updating law enhances generalization ability for optimal control.
  • The ER-based ADP method, utilizing a SPAL-based NN system identifier, demonstrates improved robustness.
  • The self-triggered mechanism effectively conserves communication compared to event-triggered methods.

Conclusions:

  • The novel self-triggered ADP framework effectively achieves optimal control for unknown nonlinear systems.
  • The integration of ER and SPAL leads to enhanced robustness and efficiency.
  • The proposed method significantly reduces communication requirements, validated through simulation.