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Abstract
This paper aims to address finite-time consensus problems for multi-agent systems under the iterative learning control framework. Distributed iterative learning protocols are presented, which adopt the terminal laws to update the control input and are offline feedforward design approaches. It is shown that iterative learning protocols can guarantee all agents in a directed graph to reach the finite-time consensus. Furthermore, the multi-agent systems can be enabled to achieve a finite-time consensus at any desired terminal state/output if iterative learning protocols can be improved by introducing the desired terminal state/output to a portion of agents. Simulation results show that iterative learning protocols can effectively accomplish finite-time consensus objectives for both first-order and higher order multi-agent systems.
Acknowledgements
The authors would like to thank the anonymous reviewers for their constructive comments and insightful suggestions which greatly improved the presentation of this paper, and also thank Dr X. Chen and Dr B. Zhang, Beihang University, P. R. China, for their useful discussions. This work was supported by the National 973 Program (2012CB821200, 2012CB821201), NSFC (61104011, 61134005, 61203044), MOE (20111102120031), Beijing Natural Science Foundation (4122046, 4132040) and Fundamental Research Funds for the Central Universities.
Additional information
Notes on contributors
Deyuan Meng
Deyuan Meng was born in Shandong, China. He received his BS degree in Mathematics and Applied Mathematics from the Ocean University of China (OUC), Qingdao, China, in June 2005, and his PhD degree in Control Theory and Control Engineering from the Beihang University (BUAA), Beijing, China, in July 2010. He is currently with the Seventh Research Division and the Department of Systems and Control, Beihang University, Beijing, China. His research interests include ILC and distributed control of multi-agent systems.
Yingmin Jia
Yingmin Jia received his BS degree in Control Theory from the Shandong University, Ji’nan, China, in January 1982, and his MS and PhD degrees both in Control Theory and Applications from the Beihang University (BUAA), Beijing, China, in 1990 and 1993, respectively. In 1993, he joined the Seventh Research Division at the Beihang University, where he is currently a Professor of Automatic Control. From February 1995 until February 1996, he was a Visiting Professor with the Institute of Robotics and Mechatronics of the German Aerospace Center (DLR), Oberpfaffenhofen, Germany. He held an Alexander von Humboldt (AvH) research fellowship with the Institute of Control Engineering at the Technical University Hamburg-Harburg, Hamburg, Germany, from December 1996 until March 1998, and a JSPS research fellowship with the Department of Electrical and Electronic Systems at the Osaka Prefecture University, Osaka, Japan, from March 2000 until March 2002. He was a Visiting Professor with the Department of Statistics at the University of California Berkeley from December 2006 until March 2007. His current research interests include robust control, adaptive control and intelligent control, and their applications in industrial processes and vehicle systems. He is the author and co-author of numerous papers and of the book ‘Robust H∞ Control’ (Science Press, 2007).
Junping Du
Junping Du was born in Beijing, China. She received her PhD degree in Computer Science from the University of Science and Technology Beijing (USTB), and then held a postdoc fellowship with the Department of Computer Science, Tsinghua University, Beijing, China. She joined the School of Computer Science, Beijing University of Posts and Telecommunications (BUPT) in July 2006, where she is currently working as a Professor in Computer Science. She served as a chair and co-chair of IPC for many international and domestic academic conferences, and has been the Vice General Secretary of the Chinese Association for Artificial Intelligence (CAAI) since 2004. She was a Visiting Professor with the Department of Computer Science, Aarhus University, Denmark, from September 1996 until September 1997. Her current research interests include artificial intelligence, data mining, intelligent management system development and computer applications.