Model reduction methods for complex network systems

X Cheng, JMA Scherpen - Annual Review of Control, Robotics …, 2021 - annualreviews.org
Network systems consist of subsystems and their interconnections and provide a powerful
framework for the analysis, modeling, and control of complex systems. However, subsystems …

Graph-theoretic analysis of power systems

T Ishizaki, A Chakrabortty, JI Imura - Proceedings of the IEEE, 2018 - ieeexplore.ieee.org
In this paper, we present an overview of the applications of graph theory in power system
modeling, dynamics, coherency, and control. First, we study synchronization of generator …

[HTML][HTML] The role of the license plate lottery policy in the adoption of Electric Vehicles: A case study of Bei**g

C Zhuge, B Wei, C Shao, Y Shan, C Dong - Energy policy, 2020 - Elsevier
Policy is an influential factor to the purchase and usage of Electric Vehicles (EVs). This
paper is focused on the license plate lottery policy, a typical vehicle purchase restriction in …

Reduction of second-order network systems with structure preservation

X Cheng, Y Kawano… - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
This paper proposes a general framework for structure-preserving model reduction of a
second-order network system based on graph clustering. In this approach, vertex dynamics …

Clustering approach to model order reduction of power networks with distributed controllers

X Cheng, JMA Scherpen - Advances in Computational Mathematics, 2018 - Springer
This paper considers the network structure preserving model reduction of power networks
with distributed controllers. The studied system and controller are modeled as second-order …

Nonlinear model reduction by deep autoencoder of noise response data

K Kashima - 2016 IEEE 55th conference on decision and …, 2016 - ieeexplore.ieee.org
In this paper a novel model order reduction method for nonlinear systems is proposed.
Differently from existing ones, the proposed method provides a suitable non-linear …

Model-free optimal control of linear multiagent systems via decomposition and hierarchical approximation

G **g, H Bai, J George… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Designing the optimal linear quadratic regulator (LQR) for a large-scale multiagent system is
time consuming since it involves solving a large-size matrix Riccati equation. The situation is …

Model reduction of multiagent systems using dissimilarity-based clustering

X Cheng, Y Kawano… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
This technical note investigates a model reduction scheme for large-scale multiagent
systems. The studied system is composed of identical linear subsystems interconnected by …

Fast online reinforcement learning control using state-space dimensionality reduction

T Sadamoto, A Chakrabortty… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
In this article, we propose a fast reinforcement learning (RL) control algorithm that enables
online control of large-scale networked dynamic systems. RL is an effective way of …

Balanced truncation of networked linear passive systems

X Cheng, JMA Scherpen, B Besselink - Automatica, 2019 - Elsevier
This paper studies model order reduction of multi-agent systems consisting of identical
linear passive subsystems, where the interconnection topology is characterized by an …