2024 8th International Workshop on Advanced Algorithms and Control Engineering(IWAACE 2024)




Prof. Long Cheng

Chinese Academy of Sciences, China

Long Cheng (Fellow, IEEE) received the B.S. degree (Hons.) in control engineering from Nankai University, Tianjin, China, in 2004, and the Ph.D. degree (Hons.) in control theory and control engineering from the Institute of Automation, Chinese Academy of Sciences, Beijing, China, in 2009. He is currently a Professor with the State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences. He is also a Professor with the University of Chinese Academy of Sciences, Beijing. He has published more than 100 technical papers in peer-refereed journals and prestigious conference proceedings. His research interests include rehabilitation robot, intelligent control, and neural networks. Dr. Cheng was a recipient of IEEE Transactions on Neural Networks Outstanding Paper Award from the IEEE Computational Intelligence Society, the Aharon Katzir Young Investigator Award from the International Neural Networks Society, and the Young Researcher Award from the Asian Pacific Neural Networks Society. He is an Associate Editor of IEEE Transactions on Cybernetics, IEEE Transactions on Automation Science and Engineering, Science China Technological Sciences, and Acta Automatica Sinica.

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Prof. Simon X. Yang

University of Guelph, Canada

Prof. Simon X. Yang received the B.Sc. degree in engineering physics from Beijing University, China in 1987, the first of two M.Sc.  degrees in biophysics from Chinese Academy of Sciences, Beijing, China in 1990, the second M.Sc. degree in electrical engineering from the University of Houston, USA in 1996, and the Ph.D. degree in electrical and computer engineering from the University of Alberta, Edmonton, Canada in 1999. Prof. Yang joined the School of Engineering at the University of Guelph, Canada in 1999. Currently he is a Professor and the Head of the Advanced Robotics & Intelligent Systems (ARIS) Laboratory at the University of Guelph in Canada. 

Prof. Yang has diversified research expertise. His research interests include intelligent systems, robotics, control systems, sensors and multi-sensor fusion, wireless sensor networks, intelligent communications, intelligent transportation, machine learning, and computational neuroscience. He has published over 600 academic papers, including over 350 journal papers. Prof. Yang he has been very active in professional activities. He serves as the Editor-in-Chief of Intelligence & Robotics, and International Journal of Robotics and Automation; and an Associate Editor of IEEE Transactions on Cybernetics, IEEE Transactions on Artificial Intelligence, and several other journals. He has been involved in the organization of many international conferences.

Title:Bio-inspired Intelligent Control of Autonomous Underwater Vehicles

Abstract:Intelligent control of autonomous underwater vehicles (AUVs) are essentially important for many important applications such as security surveillance, environmental monitoring and exploration in rivers, lakes, and seas, particularly in unstructured, hazardous and complex environments. In this talk, a bio-inspired neural network system is first developed for real-time AUV path planning with the effect of dynamic currents and unpredictable obstacles by integrating a velocity synthesis algorithm. Then the bio-inspired neural network is extended to dynamic task assignment and path planning in a multi-AUV system, where optimal results are obtained through a priority list derived by path distances deducted based on the neural network. After that, efficient and robust trajectory tracking control of AUV without actuator saturation is developed based on fuzzy logic considering the environmental noise. Finally, consensus formation tracking control of multi-AUV systems is developed using distributed bioinspired sliding mode control, with guaranteed Lyapunov stability and smooth torque inputs. Extensive simulation experiments have verified the effectiveness and efficiency of the developed intelligent methods in applications to specific AUV systems with comparisons to existing mainstream methodologies. 


Prof. Yajun Liu

South China University of Technology, China

Prof. Yajun Liu was born on September 20, 1974 in Jiangxi, China. Native speaker of Chinese, fluent in English. His Education and Academic Research Experiences is as follows: December, 2016- Now Professor in South China University of Technology School of Mechanical and Automotive Engineering. December, 2009- December, 2010. Visiting Professor in Fluid Power Research Center (FPRC) Purdue University at West Lafayette, USA. Feb, 2005 – July, 2016. Post-doctoral Research Fellow, Tokheim JV company in China. June, 2002 Ph. D. in Mechanical Engineering. South China University of Technology, Guangzhou,China. 

His research interests include Digital signal processing technology and its application in mechanical systems (such as hydraulic System for Energy Saving.); Intelligence control and Manufacturing Engineering. Moreover, Prof. Yajun Liu has published more than 270 papers in Journals and proceedings of international conferences. 40+ patents on Mechanical System design and manufacturing. My tentative title of presentation:signal processing method for Coriolis mass flowmeter based on deep learning.

Title:An Intelligent Fire Detection Technology Based on Acceleration Signal and Machine Learning

Abstract:Fire is a common and destructive disaster in modern society, and traditional fire detection methods have limitations in terms of accuracy and speed. In this study, an artificial intelligence-based fire detection technique is proposed, which utilizes the vibration features of fireproof materials during combustion. Signal processing techniques, such as time-domain analysis and wavelet packet decomposition, are used to analyze the acceleration signals generated during burning and identify unique features that distinguish fire signals from other disturbances. Machine learning algorithms are then applied to train the feature data and perform parameter tuning to optimize the detection performance. The effectiveness of the method is validated through simulated fire experiments, demonstrating that the technique can detect actual fire signals more quickly and accurately than traditional methods. This proposed method provides a new perspective for fire detection technology and has the potential to minimize the damage caused by fires. 


Prof. Chun-Yi Su

Concordia University, Canada

Dr. Chun-Yi Su received his Ph.D. degrees in control engineering from South China University of Technology in 1990. After a seven-year stint at the University of Victoria, he joined the Concordia University in 1998, where he is currently a Professor of Mechanical and Industrial Engineering and Honorary Concordia University Research Chair. His research covers control theory and its applications to various mechanical systems, with a focus on control of systems involving hysteresis nonlinearities. He is the author or co-author of over 500 publications, which have appeared in journals, as book chapters and in conference proceedings. He has been identified as Highly Cited Researchers from Clarivate since 2019. 

Dr. Su has served as Associate Editor for several journals, including IEEE Transactions on Automatic Control, IEEE Transactions on Control Systems Technology, IEEE Transactions on Cybernetics, and several other journals. He is a Distinguished Lecturer of IEEE RA Society. He served for many conferences as an Organizing Committee Member, including the General Chairs and Program Chairs. 


Prof. Ning Sun

Nankai University, China

Ning Sun received the B.S. degree in measurement & control technology and instruments (with honors) from Wuhan University, Wuhan, China, in 2009, and the Ph.D. degree in control theory and control engineering (with honors) from Nankai University, Tianjin, China, in 2014. He is a Senior Member of the IEEE. He is currently a Professor with the Institute of Robotics and Automatic Information Systems, Nankai University, Tianjin, China. His research interests include intelligent control for mechatronic/robotic systems with an emphasis on (industrial) applications.

Dr. Sun received the Machines 2021 Young Investigator Award, the prestigious Japan Society for the Promotion of Science (JSPS) Postdoctoral Fellowship for Research in Japan (Standard), the Wu Wenjun Artificial Intelligence Excellent Youth Award in 2019, the ICCAR 2022 Young Scientist Award, the China 10 Scientific and Technological Developments in Intelligent Manufacturing of 2019, several outstanding journal/conference paper awards, etc. He serves as an Associate Editor for several journals, including IEEE Transactions on Industrial Electronics, IEEE Transactions on Systems, Man, and Cybernetics: Systems, IEEE Transactions on Intelligent Transportation Systems, IEEE Systems Journal, and Journal of Field Robotics. In addition, he has been an Associate Editor of the IEEE CSS Conference Editorial Board since July 2019, and he is/was an Associate Editor for the top robotics conferences IEEE ICRA and IEEE/RSJ IROS.

Title: Intelligent Transportation Control for Underactuated Crane Systems With Applications

Abstract: As heavy industrial engineering machines, cranes have been playing very important roles in various fields, such as logistics, construction, metallurgy, and manufacturing, among others. The major task for cranes is to transport cargos from their initial positions to desired locations rapidly and accurately, with negligible swing. At present, most cranes used in practice are manipulated by human operators, which exhibits such drawbacks as low efficiency, poor anti-swing performance, incorrect operations, and high risks. Therefore, the problem of anti-swing positioning control for cranes important both theoretically and practically. Cranes are typically underactuated systems, i.e., they have fewer control inputs than their degrees of freedom (DoFs), making their control problem challenging. In this presentation, I will first share some of our recent results on dynamics analysis, motion planning, and intelligent control of different crane systems, including overhead cranes, rotary cranes, tower cranes, ship-mounted cranes, etc., with hardware experiments and applications. Then, some of our extended and related researches on robotic systems with similar dynamic characteristics will also be discussed briefly, including self-balance robots, pneumatic artificial muscle (PAM)-actuated robots, metal ingot polishing-oriented industrial robots, and so on.