Please use this identifier to cite or link to this item: https://doi.org/10.3182/20130708-3-CN-2036.00087
Title: Iterative learning based control and optimization for large scale systems
Authors: Xu, J.-X. 
Yang, S.
Issue Date: 2013
Citation: Xu, J.-X.,Yang, S. (2013). Iterative learning based control and optimization for large scale systems. IFAC Proceedings Volumes (IFAC-PapersOnline) 13 (PART 1) : 74-81. ScholarBank@NUS Repository. https://doi.org/10.3182/20130708-3-CN-2036.00087
Abstract: In this paper, we report our recent advancements in the area of iterative learning based control and optimization for large scale systems. Iterative learning control (ILC) for large scale systems consists of two categories. One is that the subsystems are physically interconnected and each subsystem has its own control objective. The other is that each subsystem is isolated, but the control objective is defined at the group level. Two sets of control schemes are described to solve these types of problems respectively. Whereas, in the area of iterative learning (IL) based optimization, parameter optimization and random perturbation based searching algorithms are presented. Finally, application examples in multi-agent systems control, power network dispatch, and freeway traffic network scheduling are discussed to demonstrate the merits of iterative learning methods. © IFAC.
Source Title: IFAC Proceedings Volumes (IFAC-PapersOnline)
URI: http://scholarbank.nus.edu.sg/handle/10635/83872
ISSN: 14746670
DOI: 10.3182/20130708-3-CN-2036.00087
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