Reinforcement Learning-Based Near Optimization for Continuous-Time Markov Jump Singularly Perturbed Systems-Reference-Cited by-同舟云学术

Reinforcement Learning-Based Near Optimization for Continuous-Time Markov Jump Singularly Perturbed Systems

Published:2023-06 Issue:6 Volume:70 Page:2026-2030
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 Jing¹^ORCID,Peng Chuanjun¹^ORCID,Park Ju H.²^ORCID,Shen Hao¹^ORCID,Shi Kaibo³^ORCID

Affiliation:

1. Anhui Province Key Laboratory of Special Heavy Load Robot and the School of Electrical and Information Engineering, Anhui University of Technology, Maanshan, China

2. Department of Electrical Engineering, Yeungnam University, Gyeongsan, Republic of Korea

3. School of Information Science and Engineering, Chengdu University, Chengdu, China

Funder

National Natural Science Foundation of China

Natural Science Foundation for Distinguished Young Scholars of Higher Education Institutions of Anhui Province

National Research Foundation of Korea (NRF) Grant

Korean Government

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/8920/10138208/10004979.pdf?arnumber=10004979

Reference18 articles.

1. Online reinforcement learning multiplayer non-zero sum games of continuous-time Markov jump linear systems

2. Optimal tracking control of nonlinear partially-unknown constrained-input systems using integral reinforcement learning

3. Computational adaptive optimal control for continuous-time linear systems with completely unknown dynamics

4. Online adaptive optimal control for continuous‐time Markov jump linear systems using a novel policy iteration algorithm

5. Parallel algorithms for optimal control of weakly coupled and singularly perturbed jump linear systems

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