Impact Score 5.78
The interplay between network science and control theory makes it possible to learn and regulate the collective behaviors in nature and human society. On the other hand, scientists and engineers have investigated the underlying principles in these collective behaviors and used them to build networked systems such as mobile robots and unmanned aerial vehicles. Therefore, research activities about networked systems have been very active in the past decade. Networked systems analysis and synthesis have grown to be some of the most promising and attractive topics in the systems and control community.
To make networked systems work together efficiently, besides their dynamics, one needs to study the local interconnections among neighbouring systems and deploy local control actions to each system in a network. Distributed state estimation of sensor networks is a specific research topic of networked systems, which can provide real-time information on the states of the estimated targets via the consensus-based information communication. The consensus and distributed nature of information communication and estimation have led to reduced communication costs and enhanced network robustness against possible failures and attacks. Thus the research of distributed estimation and control in networked systems has received considerable attention in recent years, and this trend is still growing. The consensus-based Kalman filter is a celebrated technique for distributed state estimation, which appeared at the beginning of this century. Another approach on the distributed state estimation springs up, named the distributed observer, where a network of sensors estimates the target system’s state. This new technique fuels the research on distributed state estimation greatly. Various consensus protocols have been developed for the two kinds of distributed state estimation approaches to cater to different estimation requirements.
It is natural to adapt and apply distributed state estimation algorithms to control applications. For investigations on the control of networked systems whose states cannot be measured, estimated state-based protocols are employed to guide the agents to achieve an agreement---one of the most typical distributed state estimation applications. With the advancement of distributed state estimation, the research on co-designing control protocols for the networked systems is a timely theme for extending distributed systems theory’s applicability to practical situations. Apart from incorporating distributed estimation to control networked systems, the approach is also crucial to applications, including process monitoring, surveillance, search and rescue, water distribution, and electrical power transmission.
The special issue will focus on algorithm construction and cooperative behavior analysis of networked systems based on measurements and apply observer-based distributed algorithms to networked systems. It will provide an excellent platform for developing theories of distributed estimation and its applications of networked systems.
Topics covered include, but are not limited to the following:
l Distributed state estimation of static or mobile sensor networks;
l Security analysis on distributed state estimation;
l Optimization-based distributed state estimation;
l Learning-based frameworks on distributed state estimation;
l Distributed state estimation based control;
l Applications of distributed estimation based cooperative algorithms
Before submission, authors should carefully read over the journal’s Author Guidelines, which can be found via the link https://www.elsevier.com/journals/journal-of-the-franklin-institute/ /guide-for-authors. Prospective authors should submit an electronic copy of their complete manuscript through the journal Manuscript Tracking System at https://www.editorialmanager.com/FI/default.aspx according to the following timelines:
October 31, 2021
First Round of Reviews
December 31, 2021
March 30, 2022
Lead Guest Editor
Housheng Su, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China; Email: [email protected]
Xiaoling Wang, College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023, P.R. China; Email: [email protected]
Zhiwei Gao, Faculty of Engineering and Environment, University of Northumbria, Newcastle upon Tyne, U.K.; Email: [email protected]
Ali Zemouche, University of Lorraine, CRAN CNRS UMR 7039, Cosnes-et-Romain 54400, France; Email: [email protected]
Zhiyong Chen, School of Electrical Engineering and Computing, The University of Newcastle, Callaghan, NSW 2308, Australia; Email: [email protected]
Housheng Su received the Ph.D. degree in control science and engineering from Shanghai Jiao Tong University, Shanghai, China, in 2008. In 2009-2010, he was a Postdoctoral Researcher with the City University of Hong Kong. Since 2014, he has been a professor with Huazhong University of Science and Technology. His research interests include cooperative control of networked multi-agent systems and coordinated control of multiple unmanned surface vessels. His h-index is 36. Prof. Su is an associate editor of the IET Control Theory & Applications, a Guest Editor for the Journal of the Franklin Institute and the IET Control Theory & Applications, a Leader Guest Editor for the Mathematical Problems in Engineering, and an Associate Editor for the IEEE Conference on Decision and Control, the Asian Control Conference, the American Control Conference and the Chinese Control Conference. He is a reviewer for American Mathematical Review and an active reviewer for many international journals such as Automatica, IEEE Transactions on Automatic Control, IEEE Transactions on Control Systems Technology, IEEE Transactions on Cybernetics, IEEE Transactions on Robotics, and many international conferences such as IFAC World Congress, IEEE Conference on Decision and Control, American Control Conference and Chinese Control Conference.
List of Publications
1. Housheng Su, Xiaofan Wang and Zongli Lin, Flocking of multi-agents with a virtual leader, IEEE Transactions on Automatic Control, 54(2):293-307, 2009.
2. Housheng Su, Guanrong Chen, Xiaofan Wang and Zongli Lin, Adaptive second-order consensus of networked mobile agents with nonlinear dynamics, Automatica, 47(2): 368-375, 2011.
3. Housheng Su, Han Wu and James Lam, Positive edge-consensus for nodal networks via output feedback, IEEE Transactions on Automatic Control, 64 (3): 1244-1249, 2019.
4. Mingkang Long, Housheng Su (corresponding author) and Zhigang Zeng, Output-feedback global consensus of discrete-time multiagent systems subject to input saturation via Q-learning method, IEEE Transactions on Cybernetics (2020), doi: 10.1109/TCYB.2020.2987385.
5. Mingkang Long, Housheng Su (corresponding author) and Zhigang Zeng, Model-free algorithms for containment control of saturated discrete-time multi-agent systems via Q-learning method, IEEE Transactions on Systems, Man, and Cybernetics: Systems (2020), doi: 10.1109/TSMC.2020.3019504.
Xiaoling Wang received the Ph.D. degree in control science and engineering from Shanghai Jiao Tong University, Shanghai, China, in 2017. In 2015-2017, she was a Research Associate with the CityUniversity of Hong Kong and the University of Hong Kong. Since 2017, she has joined Nanjing University of Posts and Telecommunications. Her research interests include robust control of uncertain multi-agent systems and coordination control of nonlinear networked systems. Her h-index is 13. She was a Guest Editor of Mathematical Problems in Engineering. She is a reviewer for many international journals such as IEEE Transactions on Automatic Control, IEEE Transactions on Cybernetics, Journal of the Franklin Institute, IET Control Theory & Applications, Neurocomputing and many international conferences such as IFAC World Congress, IEEE Conference on Decision and Control and Chinese Control Conference.
List of Publications
1． Xiaoling Wang, Guo-Ping Jiang, Housheng Su, Zhigang Zeng, Consensus of continuous-time linear multi-agent systems with discrete measurements, IEEE Transactions on Cybernetics, doi: 10.1109/TCYB.2020.3010520.
2． Xiaoling Wang, Guo-Ping Jiang, Housheng Su, Xiaofan Wang, Robust global coordination of networked systems with input saturation and external disturbances, IEEE Transactions on Systems, Man, and Cybernetics: Systems, doi: 10.1109/TSMC.2020.2980295.
3． Xiaoling Wang, Xiaofan Wang, Housheng Su, James Lam, Coordination control for uncertain networked systems using interval observers, IEEE Transactions on Cybernetics, 50(9): 4008-4019, 2020.
4． Xiaoling Wang, Xiaofan Wang, Housheng Su, Guanrong Chen, Fully distributed event-triggered consensus of multi-agent systems with input saturation, IEEE Transactions on Industrial Electronics, 64(6): 5055-5064, 2017.
5． Xiaoling Wang, Housheng Su, Michael Z. Q. Chen, Xiaofan Wang, Observer-based robust coordinated control of multi-agent systems with input saturation, IEEE Transactions on Neural Networks and Learning Systems, 29(5): 1933-1946, 2018.
Zhiwei Gao works with the Faculty of Engineering and Environment at the University of Northumbria, U.K., as Reader and Head of Electrical Power and Control Systems Research Group. His research interests include data-driven modelling, estimation and filtering, fault diagnosis, fault-tolerant control, distribution estimation and control, complex industrial systems, power electronics, wind energy systems, and electrical vehicles. His Google Scholar index is 33. Dr. Gao is the associate editor and editorial member of over 10 referred journals including IEEE Transactions on Automatic Control, IEEE Transactions on Industrial Electronics, IEEE Transactions on Industrial Informatics, ISA Transactions, and Renewable Energy.
List of Publications
1. Xiaoxu Liu, Zhiwei Gao, Aihua Zhang, Observer-based fault estimation and tolerant control for stochastic Takagi–Sugeno fuzzy systems with Brownian parameter perturbations, Automatica, 102: 137-149, 2019.
2. Reihane Rahimilarki, Zhiwei Gao, Aihua Zhang, Richard Binns, Robust neural network fault estimation approach for nonlinear dynamic systems with applications to wind turbine systems, IEEE Transactions on Industrial Informatics, 15: 6302-6312, 2019.
3. Xiaoxu Liu, Zhiwei Gao, Michael Z.Q. Chen, Takagi-Sugeno fuzzy model based fault estimation and signal compensation with application to wind turbines, IEEE Transactions on Industrial Electronics, 63(7): 5678-5689, 2017.
4. Zhiwei Gao, Xiaoxu Liu, Michael Z.Q. Chen, Unknown input observer based robust fault estimation for systems corrupted by partially-decoupled disturbances, IEEE Transactions on Industrial Electronics, 63(4): 2537-2547, 2016.
5. Zhiwei Gao, Carlo Cecati, Steven X. Ding, A survey of fault diagnosis and fault-tolerant techniques Part II: fault diagnosis with knowledge-based and hybrid/active approaches, IEEE Transactions on Industrial Electronics, 62(6): 3768 -3774, 2015.