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RAPIDLY-DEPLOYABLE, SELF-TUNING, SELF-RECONFIGURABLE, NEARLY-OPTIMAL CONTROL DESIGN FOR LARGE-SCALE NONLINEAR SYSTEMS (AGILE)
Start date: Sep 1, 2010, End date: Nov 30, 2013 PROJECT  FINISHED 

The inability of existing theoretical and practical tools to scaleably and efficiently deal with the control of complex, uncertain and time-changing large-scale systems, not only leads to a effort-, time- and cost-consuming deployment of Large-Scale Control Systems (LSCSs), but also prohibits the wide application of LSCS in areas and applications where LSCSs could potentially have a tremendous effect in improving system efficiency and Quality of Services (QoS), reducing energy consumption and emissions, and improving the day-to-day quality of life.Based on recent advances of its partners on convex design for LSCSs and robust and efficient LSCS self-tuning, the AGILE project aims at developing and evaluating an integrated LSCS-design methodology, applicable to large-scale systems of arbitrary scale, heterogeneity and complexity and capable of:- Providing proactive, arbitrarily-close-to-optimal LSCS performance;- Being intrinsically self-tuneable, able to rapidly and efficiently optimize LSCS performance when short- medium- and long-time variations affect the large-scale system;- Providing efficient, rapid and safe fault-recovery and LSCS re-configuration; and,- Achieving all the above, while being scalable and modular.To ease implementation and deployment of the AGILE system in existing open-architecture SCADA/DCS infrastructures, a set of open-source interfacing tools will be developed. The integrated LSCS design system to be developed within AGILE along with the interfaces will be extensively tested and evaluated into two real-life large-scale Test Cases (a 20-junction urban traffic network and a large-scale energy-controlled building) possessing a rich variety of design and performance characteristics, extremely complex nonlinear dynamics, highly stochastic effects, uncertainties and modeling errors, as well as reconfiguration and modular design requirements.
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