並列タイトル等Control theory for large-scale dynamical network systems based on model reduction techniques
一般注記This thesis provides a line of work for development of control theory for large-scale dynamical network systems such as distributed parameter systems and electric power networks. An observer and a controller for a large-scale network system are necessarily required to be low-dimensional compared with systems of interest and to guarantee an a priori performance of the whole network system. We consider constructing observers
and controllers not only satisfying the above two requirements, but also having additional properties suitable for large-scale network systems. More specifically, we propose a novel low-dimensional observer to estimate average behavior of network systems from a
macroscopic point of view where a set of states capturing the average behavior is systematically
determined. Furthermore, we propose low-dimensional hierarchical distributed
control where compositional controllers can be designed in a distributed manner. In
contrast to existing distributed controller design methods where all compositional controllers have to be designed simultaneously, the distributed design property enables us to implement a control system in particular for a large-scale network system involving a number of subsystems. These proposed observers/controllers are expected to be useful for applications in various research fields, e.g., weather prediction and data-assimilation in meteorological engineering, and supply-demand balancing of power systems in electric power engineering.
identifier:oai:t2r2.star.titech.ac.jp:50264278
連携機関・データベース国立情報学研究所 : 学術機関リポジトリデータベース（IRDB）（機関リポジトリ）