Antidisturbance Control for Hidden Markovian Jump Systems: Asynchronous Disturbance Observer Approach-Reference-Cited by-同舟云学术

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

Published:2023-11 Issue:11 Volume:68 Page:6982-6989
ISSN:0018-9286
Container-title:IEEE Transactions on Automatic Control
language:
Short-container-title:IEEE Trans. Automat. Contr.

Author:

Song Jun¹^ORCID,Zhou Sai²^ORCID,Niu Yugang²^ORCID,Cao Zhiru³^ORCID,He Shuping⁴^ORCID

Affiliation:

1. Engineering Research Center of Autonomous Unmanned System Technology (Anhui University), Ministry of Education, Hefei, China

2. Key Laboratory of Smart Manufacturing in Energy Chemical Process (East China University of Science and Technology), Ministry of Education, Shanghai, China

3. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China

4. Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Anhui Higher Education Institutions of China

Anhui Provincial Key Research and Development Project

University Synergy Innovation Program of Anhui Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/9/10298015/10042065.pdf?arnumber=10042065

Reference34 articles.

1. A Hybrid Sliding Mode Control Scheme of Markovian Jump Systems via Transition Rates Optimal Design

2. Dissipative control for nonlinear Markovian jump systems with actuator failures and mixed time-delays

3. Co-Design of 2-D Event Generator and Sliding Mode Controller for 2-D Roesser Model via Genetic Algorithm

4. Sliding mode control for nonlinear Markovian jump systems under denial-of-service attacks

5. Observer-Based Adaptive Sliding Mode Control for Nonlinear Stochastic Markov Jump Systems via T–S Fuzzy Modeling: Applications to Robot Arm Model

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