Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICSMC.2012.6378173
Title: Adaptive Iterative learning control for multi-agent systems consensus tracking
Authors: Yang, S.
Xu, J.-X. 
Keywords: Adaptive iterative learning control
Consensus
Multi-agent systems
Issue Date: 2012
Source: Yang, S.,Xu, J.-X. (2012). Adaptive Iterative learning control for multi-agent systems consensus tracking. Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics : 2803-2808. ScholarBank@NUS Repository. https://doi.org/10.1109/ICSMC.2012.6378173
Abstract: This paper addresses an adaptive iterative learning control (AILC) based scheme for multi-agent systems (MAS) consensus tracking under repeatable control environment. The agent dynamics are assumed to be inherently nonlinear with unknown time-varying parameters. The underline communication among followers is fixed and undirected. The leader's trajectory is dynamically changing, and only available to a small portion of followers. By utilizing the repetitiveness of the tracking task, the unknown time-varying parameters can be effectively estimated, and the leader's velocity is not required in the controller. Under either resetting condition or alignment condition, perfect consensus tracking for the MAS can be achieved asymptotically in the iteration domain. A simulation example is given to demonstrate the effectiveness of the proposed algorithm. © 2012 IEEE.
Source Title: Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
URI: http://scholarbank.nus.edu.sg/handle/10635/69184
ISBN: 9781467317146
ISSN: 1062922X
DOI: 10.1109/ICSMC.2012.6378173
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