In the rising trend of Industry 4.0, manufacturing industries have been experiencing significant changes with the increased untilization of machine learning, big data, aritificial intelligence, and intelligent automation. Modern industrial equipments and systems have been intensively used in wide applications to achieve a higher level of automation, e.g., for smart grids, renewable energy systems, robots, transportation and autotomotive industries. These changes requires better performance of the industrial systems in terms of robustness, reliablity, design and implementation simplicity, and intelligence.
Sliding mode control (SMC), as an efficacious and powerful control methodology, is playing an essential role in meeting the performance requirements for modern industrial systems. The merits of SMC are high robustness against disturbances and parameter variations, reduced-order system design, simple control structure, computational simplicity for implementation, and fast dynamic response. Academics and engineers are working on further improving the convergence and robustness peformance, resulting in the dramatic development of the SMC methods. In spite of various research, the major technical problems of SMC are still challenging, particularly for modern industrial systems. As such, much of the recent SMC research has focused on the intergration with soft computing (SC) technologies, such that not only is the SMC more intelligent and flexible facing complex industrial environment, but also stronger robustness can be ensured. Meanwhile, the latest advances of microcontrollers, digital signal processors, sensors, etc. also facilitate the practical implementation of adavanced and intelligent SMC designs for complex industrial systems.
The aim of this Special Section is to focus on the latest developments in the SC-based intelligent SMC for industrial systems, such as fuzzy logic (FL)-based SMC, neural network (NN)-based SMC, probabilistic reasoning (PR)-based SMC, SC integration-based SMC, etc. Meanwhile, practical technical issues and challenges of the intelligent SMC in various industrial applications should also be addressed.
Suggested topics include:
FL-based SMC techniques
NN-based SMC techniques
PR-based SMC techniques (evolutionary algorithms, chaos theory, belief networks, etc)
SC methodology integration-based SMC techniques
Intelligent sliding mode observer design techniques
Applications of intelligent SMC in transportation, robots, automotive systems, mechatronic systems)
Applications of intelligent SMC in industrial electronics (smart grid, renewable energy systems, power converters)
Applications of intelligent SMC in networking and communication systems
New papers, or extended versions of papers presented at related conferences, are welcome. Submissions must not be currently under review for publication elsewhere. Conference papers may be submitted only if they are substantially extended (more than 50%), and must be referenced. All submitted papers will be peer-reviewed using the normal standards of CAEE, and accepted based on quality, originality, novelty, and relevance to the theme of the special section. By submitting a paper to this Special Session, the authors agree to referee one paper (if asked) within the time frame of the Special Session.
Before submission, authors should carefully read the Guide for Authors available at
Authors should submit their papers through the journal’s web submission tool at https://www.editorialmanager.com/compeleceng/default.aspx by selecting “VSI-smc” under the “Issues” tab.
For additional questions, contact the Main Guest Editor.
Submission of manuscript: Dec. 30, 2020
First notification: March 25, 2021
Submission of revised manuscript: April 30, 2021
Notification of the re-review: May 28, 2021
Final notification: June 30, 2021
Final paper due: July 30, 2021
Publication: Nov. 2021
Hai Wang, Ph.D. (Managing Guest Edtior)
Discipline of Engineering and Energy, College of Science, Health, Engineering and Education
Murdoch University, Perth, Australia
Shihong Ding, Ph.D.
School of Electrical and Information Engineering,
Jiangsu University, Zhenjiang, China
Kanghyun Nam, Ph.D.
School of Mechanical Engineering
Yeungnam University, Gyeongbuk, South Korea
Hai Wang (Senior Member, IEEE) received his B.E. degree from Hebei Polytechnic University, China, in 2007, the M. E. degree from Guizhou University, China, in 2010, and the PhD degree from Swinburne University of Technology (SUT), Australia, in 2013, respectively, all in electrical and electronic engineering. From 2014 to 2015, he was the Postdoc Research Fellow in the Faculty of Sciences, Engineering and Technology, at SUT, Australia. From 2015 to early 2019, he was with the School of Electrical and Automation Engineering at Hefei University of Technology, China, where he served as the Full Professor (Huangshan Young Scholar) and the Deputy Discipline Head of Automation. Hai is currently the Senior Lecturer of Electrical Engineering and Academic Chair of Instrumentation & Control Engineering and Industrial Computer Systems Engineering in the College of Science, Health, Engineering and Education at Murdoch University, Perth, Australia.
Dr. Wang has published over 60 peer-reviewed international journal and conference papers, mostly in the areas of sliding mode control theory and its applications, and robotics & mechatronics. He was a committee member of a number of international conferences and workshops in Australia and China. He currently serves as an Associate Editor of IEEE ACCESS. He is also an active researcher with leading positions, such as Chair of IEEE Industrial Electronics Society Western Australia Chapter. His research interests are in sliding mode control and observer, adaptive control, robotics and mechatronics, neural networks, nonlinear systems, and vehicle dynamics & control.
Shihong Ding (Member, IEEE) was born in Anhui, China, in 1983. He received the B.E. degree in mathematics from Anhui Normal University, China, in 2004, and the M.S. and Ph.D. degrees in automatic control from Southeast University, China, in 2007 and 2010, respectively. During the graduate studies, he visited The University of Texas at San Antonio from August 2008 to August 2009. After graduation, he held a research fellowship with the University of Western Sydney for one year. He also visited Yeungnam University, South Korea, from July 2018 to August 2018 and RMIT University, from December 2019 to February 2020, respectively. Since June 2010, he has been with the School of Electrical and Information Engineering, Jiangsu University, where he is currently a Full Professor.
Prof. Ding has published more than 70 peer-reviewed international journal and conference articles, in the area of sliding mode control theory and its application. He has been awarded the Outstanding Reviewer of Automatica, IET Control Theory and Applications in 2014 and 2015. His research interests include sliding mode control and finite-time stability. He currently serves as a Subject Editor of Nonlinear Dynamics and an Associate Editor of IEEE ACCESS.
Kanghyun Nam (Member, IEEE) received the B.S. degree in mechanical engineering from Kyungpook National University, Daegu, South Korea, in 2007, the M.S. degree in mechanical engineering from the Korea Advanced Institute of Science and Technology, Daejeon, South Korea, in 2009, and the Ph.D. degree in electrical engineering from The University of Tokyo, Tokyo, Japan, in 2012. From 2012 to 2015, he was a Senior Engineer with Samsung Electronics Co., Ltd., Gyeonggido, South Korea. Since 2015, he has been an Assistant Professor with the School of Mechanical Engineering, Yeungnam University, Gyeongbuk, South Korea.
Prof. Nam’s research interests include vehicle motion control, steer-by-wire system control, in-wheel-motor-driven electric vehicles, motion control theory and industry applications. He is an Associate Editor for the IEEE Transactions on Vehicular Technology and a member of the Korean Society of Automotive Engineers. He received the Best Paper Award from the IEEE Transactions on Industrial Electronics in 2014.