Neural Network-Based Anti-Disturbance Control of Nonhomogeneous Markov Switching Systems-Reference-Cited by-同舟云学术

Neural Network-Based Anti-Disturbance Control of Nonhomogeneous Markov Switching Systems

Published:2024-06 Issue:6 Volume:71 Page:3151-3155
ISSN:1549-7747
Container-title:IEEE Transactions on Circuits and Systems II: Express Briefs
language:
Short-container-title:IEEE Trans. Circuits Syst. II

Author:

Cheng Jun¹^ORCID,Wu Junhui¹,Yan Huaicheng²^ORCID,Wu Zheng-Guang³^ORCID,Zhang Dan⁴^ORCID

Affiliation:

1. School of Mathematics and Statistics, Guangxi Normal University, Guilin, China

2. Key Laboratory of Advanced Control and Optimization for Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China

3. Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China

4. College of Information Engineering, Zhejiang University of Technology, Hangzhou, China

Funder

NSFC

Zhejiang Provincial Natural Science Foundation of China

Key Laboratory of Interdisciplinary Research for Data Science (Guangxi Normal University), Education Department of Guangxi Zhuang Autonomous Region

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/8920/10561573/10414032.pdf?arnumber=10414032

Reference20 articles.

1. Antidisturbance Control for Hidden Markovian Jump Systems: Asynchronous Disturbance Observer Approach

2. Event-Triggered SMC for Networked Markov Jumping Systems With Channel Fading and Applications: Genetic Algorithm

3. Finite-Time Control of Markov Jump Lur'e Systems With Singular Perturbations

4. Decentralized adaptive sliding mode control of large-scale semi-Markovian jump interconnected systems with dead-zone input

5. Sliding mode control for singularly perturbed Markov jump descriptor systems with nonlinear perturbation