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Prescribed-rate target tracking for time-delayed systems using output measurements.

Chengye Zhang1, Ci Chen2, Frank L Lewis3

  • 1The School of Automation, Guangdong University of Technology, Guangzhou, China; Guangdong Provincial Key Laboratory of Intelligent Systems and Optimization Integration, Guangzhou, China.

Neural Networks : the Official Journal of the International Neural Network Society
|January 28, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel reinforcement learning method for target tracking systems with input delays. The approach ensures precise tracking performance by predicting future states using only output measurements.

Keywords:
Output measurementsReinforcement learningRobust output regulationTarget trackingTime-delayed system

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Area of Science:

  • Control Systems Engineering
  • Machine Learning
  • Robotics

Background:

  • Target tracking systems often face challenges due to input time delays.
  • Ensuring precise tracking performance in the presence of delays is critical for many applications.

Purpose of the Study:

  • To develop a reinforcement learning framework for target tracking with input time delays.
  • To ensure tracking performance at a prescribed convergence rate using only output measurements.

Main Methods:

  • The study integrates robust output regulation theory with reinforcement learning.
  • A novel approach, Future State Prediction by Input-Output Measurements, is proposed.
  • Off-policy reinforcement learning algorithms are developed to estimate future states and compensate for delays.

Main Results:

  • The proposed method effectively addresses target tracking problems with input time delays.
  • The approach unifies input time delays and output-feedback tracking under a single framework.
  • Numerical results validate the effectiveness of the reinforcement learning algorithms.

Conclusions:

  • The developed algorithms enable accurate target tracking in systems with input time delays.
  • The method relies solely on output measurements, eliminating the need for an accurate system model.
  • This research offers a robust solution for real-world tracking applications.