8 papers · 1 filter
Bilinear Koopman-Based Robust Model Predictive Control for Unknown Nonlinear Systems via Contraction Metrics
Yuki Higuchi, Kazuhiro Sato
Data-driven model predictive control (MPC) using Koopman operator theory is a promising approach for constrained control of unknown nonlinear systems. While linear Koopman realizat…
Minimal controllability problems on linear structural descriptor systems
Shun Terasaki, Kazuhiro Sato
We consider minimal controllability problems (MCPs) on linear structural descriptor systems. We address two problems of determining the minimum number of input nodes such that a de…
Controllability maximization of large-scale systems using projected gradient method
Kazuhiro Sato, Akiko Takeda
In this work, we formulate two controllability maximization problems for large-scale networked dynamical systems such as brain networks: The first problem is a sparsity constraint…
Riemannian optimal identification method for linear systems with symmetric positive-definite matrix
Kazuhiro Sato, Hiroyuki Sato, Tobias Damm
This study develops identification methods for linear continuous-time symmetric systems, such as electrical network systems, multi-agent network systems, and temperature dynamics i…
Structure-preserving optimal model reduction based on Riemannian trust-region method
Kazuhiro Sato, Hiroyuki Sato
This paper studies stability and symmetry preserving optimal model reduction problems of linear systems which include linear gradient systems as a special case. The problem i…
Riemannian optimal model reduction of stable linear systems
Kazuhiro Sato
In this paper, we develop a method for solving the problem of minimizing the error norm between the transfer functions of original and reduced systems on the set of stable ma…