Prof. Long Cheng
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Short Bio:
Long Cheng received the B.S. degree (with honors) in control engineering from NankaiUniversity, Tianjin, China, in July 2004, and the Ph.D. degree (with honors) in control theory andcontrol engineering from the Institute of Automation,Chinese Academy of Sciences, Beijing, China,in July 2009.
From February 2010 to September 2010,he was a Post-Doctoral Research Fellow at the Department of MechanicalEngineering, University of Saskatchewan, Saskatoon, SK, Canada. From September2010 to March 2011, he was a Post-Doctoral Research Fellow at theMechanical and Industrial Engineering Department, Northeastern University,Boston, MA. From December 2013 to March 2014, he was a visiting scholar at the Electrical and Computer Engineering Department, University of California, Riverside. In December 2015, he was a senior research associate at the Department of Mechanical and Automation Engineering, Chinese University of Hong Kong. Currently, he is a Professor at the Laboratory ofComplex Systems and Intelligent Science, Instituteof Automation, Chinese Academy of Sciences. He has published more than 90 technical papers in peer-refereedjournals and prestigious conference proceedings. His current research interests include intelligent control of smart materials, coordination of multi-agent systems, neural networks and their applications to robotics.
Dr. Cheng is the recipient of IEEE Transactions on Neural Networks Outstanding Paper Award. He serves as an Editorial Board Member ofNeurocomputing, Neural Processing Letters, Neural Computing & Applications, International Journal of Systems Science and Advances in Mechanical Engineering. He also serves as the Program Committee Chair of 2016 International Symposium on Neural Networks and 2017 International Conference on Information Science and Technology.
Project Summary:
The distributed coordination control of a group of manipulators has been studied extensively due to its potential applications in the industrial assembly, unknown area exploration, and coordinated rescue.The coordination problem can be roughly divided into two classes: the leaderless class (self-organization) and the leader-following class (global behavior). In most studies of the leader-following coordination, the leader manipulator is assumed to be a fixed value rather than a time-varying trajectory, which strictly limits its application. In this project, we proposed an adaptive neural-network-based leader-following control algorithm where the leader can be of dynamic trajectory. In the system, themanipulator’s dynamics includes uncertainties not satisfying the ``parameter-in-linearity'' condition. Neural networks are employed to approximate these uncertainties, and the approximation error and external disturbances are counteracted by the robust term. The communication topology among manipulators is directed and time-varying. It has been proved that when the communication topology switches among a set of directed graphs which have the spanning tree and have no loop structure, the steady-state tracking error can be reduced as small as possible if parameters in the proposed controller are appropriately chosen.
Related Publication:
Long Cheng, Yunpeng Wang, Wei Ren, Zeng-Guang Hou, Min Tan, “Containment control of multi-agentsystems with dynamic leaders based on a PIn-type approach,” IEEE Transactions on Cybernetics, in press,DOI: 10.1109/TCYB.2015.2494738, 2015.
Yunpeng Wang, Long Cheng, Zeng-Guang Hou, Junzhi Yu, Min Tan, “Optimal formation of multi-robotsystems based on a recurrent neural network,” IEEE Transactions on Neural Networks and LearningSystems, vol. 27, no. 2, pp. 322–333, 2016.
Long Cheng, Hanlei Wang, Zeng-Guang Hou, Min Tan, “Reaching a consensus in networks of high-orderintegral agents under switching directed topologies,” International Journal of Systems Science, vol. 47, no.8, pp. 1966–1982, 2016.
Long Cheng, Ming Cheng, Hongnian Yu, Lu Deng, Zeng-Guang Hou, “Distributed tracking control ofuncertain multiple manipulators under switching topologies using neural networks,” to appear in Proceedingsof the 13th International Symposium on Neural Networks, Saint Petersburg, Russia, July 6–8 , 2016.