22 citations · 102 across the 17 of their papers we have counts for
10 papers · 1 filter
Entropic Model Predictive Optimal Transport for Underactuated Linear Systems
Kaito Ito, Kenji Kashima
This letter investigates dynamical optimal transport of underactuated linear systems over an infinite time horizon. In our previous work, we proposed to integrate model predictive…
Entropic Model Predictive Optimal Transport over Dynamical Systems
Kaito Ito, Kenji Kashima
We consider the optimal control problem of steering an agent population to a desired distribution over an infinite horizon. This is an optimal transport problem over dynamical syst…
Maximum entropy optimal density control of discrete-time linear systems and Schrödinger bridges
Kaito Ito, Kenji Kashima
We consider an entropy-regularized version of optimal density control of deterministic discrete-time linear systems. Entropy regularization, or a maximum entropy (MaxEnt) method fo…
Matrix Pontryagin principle approach to controllability metrics maximization under sparsity constraints
Tomofumi Ohtsuka, Takuya Ikeda, Kenji Kashima
Controllability maximization problem under sparsity constraints is a node selection problem that selects inputs that are effective for control in order to minimize the energy to co…
Sinkhorn MPC: Model predictive optimal transport over dynamical systems
Kaito Ito, Kenji Kashima
We consider the optimal control problem of steering an agent population to a desired distribution over an infinite horizon. This is an optimal transport problem over a dynamical sy…
Sparse optimal stochastic control
Kaito Ito, Takuya Ikeda, Kenji Kashima
In this paper, we investigate a sparse optimal control of continuous-time stochastic systems. We adopt the dynamic programming approach and analyze the optimal control via the valu…
Multiple sparsity constrained control node scheduling with application to rebalancing of mobility networks
Takuya Ikeda, Kazunori Sakurama, Kenji Kashima
This paper treats an optimal scheduling problem of control nodes in networked systems. We newly introduce both the L0 and l0 constraints on control inputs to extract a time-varying…
Structured Hammerstein-Wiener Model Learning for Model Predictive Control
Ryuta Moriyasu, Taro Ikeda, Sho Kawaguchi +1
This paper aims to improve the reliability of optimal control using models constructed by machine learning methods. Optimal control problems based on such models are generally non-…
Bayesian Differential Privacy for Linear Dynamical Systems
Genki Sugiura, Kaito Ito, Kenji Kashima
Differential privacy is a privacy measure based on the difficulty of discriminating between similar input data. In differential privacy analysis, similar data usually implies that…
Noise Response Data Reveal Novel Controllability Gramian for Nonlinear Network Dynamics
Kenji Kashima
Control of nonlinear large-scale dynamical networks, e.g., collective behavior of agents interacting via a scale-free connection topology, is a central problem in many scientific a…