Topical Collection on Computational Intelligence-based Control and Estimation in Mechatronic Systems

Overview 

Modern mechatronic systems are currently experiencing immense changes in the fourth Industrial revolution with the recent advances in artificial intelligence (AI) techniques, big data analytics, cutting-edge telecommunication technologies, control theory and microelectronics. Mechatronic systems become highly multidisciplinary with an ever-increasing synergistic integration of mechanical, electrical/electronic, control and information disciplines. The complex technical changes urge the modern mechatronic systems to exhibit more stable and excellent operating performance, in terms of strong robustness and reliability, design simplicity and smartness. However, mechatronic systems are continuously facing technical challenges and difficulties, under the parametric and/or structural uncertainties, undesired external disturbances, fast-varying references, sensor noises, nonlinearities, mechanical and mechatronic component failures, and/or restricted online computing time of the control execution, etc. In order to further address the above concerns and improve the overall performance of mechatronic systems, many computational intelligence (CI) technologies (fuzzy logic, neural networks, reinforcement learning, AI, etc.) have been popularly utilized to assist with the modelling, control and estimation of mechatronic systems, which attract considerable attention among researchers and engineers from academia and industry. Meanwhile, the latest developments of sensors, microcontrollers, DSP, FPGA, and etc. also enhance the real-time CI-based control and estimation implementations of mechatronic systems.       

The main objective of this Special Issue is to highlight the recent innovations, developments and challenges in CI-based modelling, control and estimation of modern mechatronic systems and address the impressive CI contributions covering different mechatronic applications, i.e., robots, automotive systems, MEMS, nanopositioner, etc.

Topics of Interest

We welcome authors to present new techniques, methodologies, mixed method approaches and research directions unsolved issues. Topics of interest include, but are not limited to:

  • Fuzzy logic techniques for modelling, control and estimation 
  • Neural network techniques for modelling, control and estimation
  • Probabilistic reasoning algorithms for modelling, control and estimation (evolutionary algorithms, etc) 
  • Neuro-fuzzy techniques for modelling, control and estimation
  • Deep learning/reinforcement learning for modelling, control and estimation
  • Stochastic learning and statistical algorithms for modelling, control and estimation
  • Machine learning and/or AI methods for modelling, control and estimation
  • CI-based modelling, control and estimation for automotive systems (engine, chassis, powertrain, drive-by-wire vehicles, electric vehicles, autonomous vehicles, etc)
  • CI-based modelling, control and estimation for robots, MEMS, nanopositioner systems
  • CI-based connected vehicles and intelligent transportation systems
  • CI-based multi-agent robots and autonomous systems
  • CI-based fault diagnosis and prognostics of mechatronic systems
  • CI-based actuators, and sensor/data fusion systems design for mechatronic systems

We would also welcome review articles that capture the current state-of-the art and outline future areas of research in the fields relevant to this Special Issue.

Peer Review Process

All the papers will go through peer review,  and will be reviewed by at least three reviewers. A thorough check will be completed, and the guest editors will check any significant similarity between the manuscript under consideration and any published paper or submitted manuscripts of which they are aware. In such case, the article will be directly rejected without proceeding further. Guest editors will make all reasonable effort to receive the reviewer’s comments and recommendation on time.

The submitted papers must provide original research that has not been published nor currently under review by other venues. Previously published conference papers should be clearly identified by the authors at the submission stage and an explanation should be provided about how such papers have been extended to be considered for this special issue (with at least 30% difference from the original works).

Submission Guidelines:

Paper submissions for the special issue should strictly follow the submission format and guidelines (https://www.springer.com/journal/521/submission-guidelines). Each manuscript should not exceed 16 pages in length (inclusive of figures and tables).

Manuscripts must be submitted to the journal online system at https://www.editorialmanager.com/ncaa/default.aspx.
Authors should select “SI: CI-based Control and Estimation in Mechatronic Systems” during the submission step ‘Additional Information’.

Important Dates

Deadline for submissions: December 15, 2020
Deadline for review: January 30, 2021
Decisions: February 10, 2021
Deadline for revised version by authors: March 15, 2021
Deadline for 2nd review:  April 5, 2021
Final decisions: April 25, 2021

Guest Editors

Hai Wang, Ph.D. (Lead Guest Editor)
Dscipline of Engineering and Energy, College of Science, Health, Engineering and Education, Murdoch University, Perth, Australia, Email: hai.wang@murdoch.edu.au

Jinchuan Zheng, Ph.D.
Faculty of Science, Engineering and Technology,
Swinburne University of Technology, Melbourne, Australia, Email: jzheng@swin.edu.au

Yuqian Lu, Ph.D.
Department of Mechanical Engineering, The University of Auckland, New Zealand, Email: yuqian.lu@auckland.ac.nz

Shihong Ding, Ph.D. 
School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China, Email: dsh@mail.ujs.edu.cn

Hicham Chaoui, Ph.D.
Department of Electronics, Carleton University, Ottawa, Canada, Email: Hicham.Chaoui@carleton.ca