D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Electronics and Electrical Engineering D-index 30 Citations 3,749 221 World Ranking 3775 National Ranking 443

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Control theory
  • Statistics

His primary areas of study are Control theory, Control engineering, Model predictive control, Control system and Control theory. His Adaptive control, Open-loop controller and Robust control study in the realm of Control engineering connects with subjects such as Cascade. His biological study spans a wide range of topics, including Distributed control system, Stability, Networked control system, Decentralised system and Nonlinear system.

Shaoyuan Li studied Control system and Temperature control that intersect with Inverse. The Control theory study combines topics in areas such as Fuzzy logic, Mathematical optimization, Cluster analysis and System identification. His Stability research integrates issues from Control and Lyapunov function.

His most cited work include:

  • Nash-optimization enhanced distributed model predictive control applied to the Shell benchmark problem (136 citations)
  • Networked model predictive control based on neighbourhood optimization for serially connected large-scale processes (108 citations)
  • Technical Communique: A synthesis approach of on-line constrained robust model predictive control (97 citations)

What are the main themes of his work throughout his whole career to date?

The scientist’s investigation covers issues in Control theory, Model predictive control, Control theory, Mathematical optimization and Nonlinear system. Shaoyuan Li combines subjects such as Control engineering and Control with his study of Control theory. His work in the fields of Control, such as Supervisory control, intersects with other areas such as Supervisor.

His Model predictive control research is multidisciplinary, incorporating perspectives in Data-driven, Microgrid, Lyapunov function and Optimal control. Shaoyuan Li studies Control theory, focusing on PID controller in particular. Shaoyuan Li has included themes like Upper and lower bounds and Fuzzy logic in his Mathematical optimization study.

He most often published in these fields:

  • Control theory (59.25%)
  • Model predictive control (38.34%)
  • Control theory (18.23%)

What were the highlights of his more recent work (between 2018-2021)?

  • Model predictive control (38.34%)
  • Control theory (59.25%)
  • Control (10.72%)

In recent papers he was focusing on the following fields of study:

His main research concerns Model predictive control, Control theory, Control, Distributed computing and Distributed model predictive control. His Model predictive control study incorporates themes from Energy, Microgrid, Lyapunov function and Optimal control. He connects Control theory with Scale in his study.

His Supervisory control study in the realm of Control interacts with subjects such as Supervisor. His work deals with themes such as Control reconfiguration and Cyber-physical system, which intersect with Distributed model predictive control. His Control theory study combines topics in areas such as Optimization problem and Topology.

Between 2018 and 2021, his most popular works were:

  • Distributed MPC for Coordinated Energy Efficiency Utilization in Microgrid Systems (23 citations)
  • Infinite-step opacity and K-step opacity of stochastic discrete-event systems (22 citations)
  • Infinite-step opacity and K-step opacity of stochastic discrete-event systems (22 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Control theory
  • Statistics

Shaoyuan Li focuses on Model predictive control, Control theory, Opacity, Control and Supervisory control. His Model predictive control research includes elements of Control theory, Control, Mahalanobis distance and Energy management. His Control theory research is multidisciplinary, relying on both Abstraction, Robot and System model.

His work on Stability, Nonlinear system and Lyapunov function as part of general Control theory study is frequently connected to Q-learning, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. Shaoyuan Li focuses mostly in the field of Control, narrowing it down to topics relating to State and, in certain cases, Computational complexity theory. His research integrates issues of Computer network and Design objective in his study of Supervisory control.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Nash-optimization enhanced distributed model predictive control applied to the Shell benchmark problem

Shaoyuan Li;Yan Zhang;Quanmin Zhu.
Information Sciences (2005)

189 Citations

Technical Communique: A synthesis approach of on-line constrained robust model predictive control

Baocang Ding;Yugeng Xi;Shaoyuan Li.
Automatica (2004)

150 Citations

Networked model predictive control based on neighbourhood optimization for serially connected large-scale processes

Yan Zhang;Shaoyuan Li.
Journal of Process Control (2007)

140 Citations

Distributed model predictive control over network information exchange for large-scale systems

Yi Zheng;Shaoyuan Li;Ning Li.
Control Engineering Practice (2011)

127 Citations

A new coordinated control strategy for boiler-turbine system of coal-fired power plant

Shaoyuan Li;Hongbo Liu;Wen-Jian Cai;Yeng-Chai Soh.
IEEE Transactions on Control Systems and Technology (2005)

124 Citations

Multi-model predictive control based on the Takagi-Sugeno fuzzy models: a case study

Ning Li;Shao-Yuan Li;Yu-Geng Xi.
Information Sciences (2004)

124 Citations

A new terminal sliding mode control for robotic manipulators

Dongya Zhao;Shaoyuan Li;Feng Gao.
International Journal of Control (2009)

121 Citations

Normalized decoupling control for high-dimensional MIMO processes for application in room temperature control HVAC systems

Yuling Shen;Wen-Jian Cai;Shaoyuan Li.
Control Engineering Practice (2010)

117 Citations

Multiple fuzzy model-based temperature predictive control for HVAC systems

Ming He;Wen-Jian Cai;Shao-Yuan Li.
Information Sciences (2005)

117 Citations

A fuzzy goal programming approach to multi-objective optimization problem with priorities

Chao-Fang Hu;Chang-Jun Teng;Shao-Yuan Li.
European Journal of Operational Research (2007)

111 Citations

Best Scientists Citing Shaoyuan Li

Quanmin Zhu

Quanmin Zhu

University of the West of England

Publications: 31

Han-Xiong Li

Han-Xiong Li

City University of Hong Kong

Publications: 28

Akhil Garg

Akhil Garg

Huazhong University of Science and Technology

Publications: 26

Ridong Zhang

Ridong Zhang

Hangzhou Dianzi University

Publications: 12

Kwang Y. Lee

Kwang Y. Lee

Pennsylvania State University

Publications: 12

Jinhua She

Jinhua She

Tokyo University of Technology

Publications: 11

Wenjian Cai

Wenjian Cai

Nanyang Technological University

Publications: 10

Feng Ding

Feng Ding

Jiangnan University

Publications: 10

Weihua Gui

Weihua Gui

Central South University

Publications: 9

Min Wu

Min Wu

China University of Geosciences

Publications: 9

Liang Gao

Liang Gao

Huazhong University of Science and Technology

Publications: 9

Bart De Schutter

Bart De Schutter

Delft University of Technology

Publications: 9

Yugang Niu

Yugang Niu

East China University of Science and Technology

Publications: 9

Gang Tao

Gang Tao

University of Virginia

Publications: 8

Furong Gao

Furong Gao

Hong Kong University of Science and Technology

Publications: 8

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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