Model-Free Optimal Tracking Design With Evolving Control Strategies via Q-Learning-Reference-Cited by-同舟云学术

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Model-Free Optimal Tracking Design With Evolving Control Strategies via Q-Learning

Published:2024-07 Issue:7 Volume:71 Page:3373-3377
ISSN:1549-7747
Container-title:IEEE Transactions on Circuits and Systems II: Express Briefs
language:
Short-container-title:IEEE Trans. Circuits Syst. II

Author:

Wang Ding¹^ORCID,Huang Haiming¹^ORCID,Zhao Mingming¹^ORCID

Affiliation:

1. Faculty of Information Technology, the Beijing Key Laboratory of Computational Intelligence and Intelligent System, the Beijing Laboratory of Smart Environmental Protection, Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, China

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Beijing Natural Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/8920/10581876/10415874.pdf?arnumber=10415874

Reference21 articles.

1. Reinforcement learning improves behaviour from evaluative feedback

2. Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

3. Dual Event-Triggered Constrained Control Through Adaptive Critic for Discrete-Time Zero-Sum Games

4. Sliding-Mode Control for IT2 Fuzzy Nonlinear Singularly Perturbed Systems and Its Application to Electric Circuits: A Dynamic Event-Triggered Mechanism

5. System Stability of Learning-Based Linear Optimal Control With General Discounted Value Iteration

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