4 citations · 4 across the 14 of their papers we have counts for
4 papers · 1 filter
Convex Chance-Constrained Stochastic Control under Uncertain Specifications with Application to Learning-Based Hybrid Powertrain Control
Teruki Kato, Ryotaro Shima, Kenji Kashima
This paper proposes a strictly convex chance-constrained stochastic control framework that accounts for uncertainty in control specifications such as reference trajectories and ope…
Modeling and Control of Deep Sign-Definite Dynamics with Application to Hybrid Powertrain Control
Teruki Kato, Ryotaro Shima, Kenji Kashima
Data-driven control increasingly relies on deep models for complex systems whose first-principles models are difficult to obtain. For reliable deployment, however, learned dynamics…
Sampled-Data Primal-Dual Gradient Dynamics in Model Predictive Control
Ryuta Moriyasu, Sho Kawaguchi, Kenji Kashima
Model Predictive Control (MPC) is a versatile approach capable of accommodating diverse control requirements that holds significant promise for a broad spectrum of industrial appli…
Learning Exactly Linearizable Deep Dynamics Models
Ryuta Moriyasu, Masayuki Kusunoki, Kenji Kashima
Research on control using models based on machine-learning methods has now shifted to the practical engineering stage. Achieving high performance and theoretically guaranteeing the…