Predefined-Time Event-Triggered Tracking Control for Nonlinear Servo Systems: A Fuzzy Weight-Based Reinforcement Learning Scheme-Reference-Cited by-同舟云学术

Predefined-Time Event-Triggered Tracking Control for Nonlinear Servo Systems: A Fuzzy Weight-Based Reinforcement Learning Scheme

Published:2024-08 Issue:8 Volume:32 Page:4557-4569
ISSN:1063-6706
Container-title:IEEE Transactions on Fuzzy Systems
language:
Short-container-title:IEEE Trans. Fuzzy Syst.

Author:

Shen Hao¹^ORCID,Zhao Wei¹^ORCID,Cao Jinde²^ORCID,Park Ju H.³^ORCID,Wang Jing¹^ORCID

Affiliation:

1. School of Electrical and Information Engineering and Anhui Provincial Key Laboratory of Power Electronics and Motion Control, Anhui University of Technology, Ma'anshan, China

2. Mathematics Southeast University, Nanjing, China

3. Department of Electrical Engineering, Yeungnam University, Kyongsan, Republic of Korea

Funder

National Natural Science Foundation of China

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

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

Research and Development project of Engineering Research Center of Biofilm Water Purification and Utilization Technology of Ministry of Education

University Synergy Innovation Program of Anhui Province

National Research Foundation of Korea

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/91/10623556/10536605.pdf?arnumber=10536605

Reference42 articles.

1. An Adaptive Servo Control Strategy for Automotive Electronic Throttle and Experimental Validation

2. Intelligent Backstepping Control Using Recurrent Feature Selection Fuzzy Neural Network for Synchronous Reluctance Motor Position Servo Drive System

3. A novel pitch control system for a wind turbine driven by a variable-speed pump-controlled hydraulic servo system

4. Distributed Model-Free Adaptive Control for Learning Nonlinear MASs Under DoS Attacks

5. Optimized Backstepping Consensus Control Using Reinforcement Learning for a Class of Nonlinear Strict-Feedback-Dynamic Multi-Agent Systems