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eess.SY2026
Model Predictive Path Integral PID Control for Learning-Based Path Following
Teruki Kato, Koshi Oishi, Seigo Ito
Classical proportional--integral--derivative (PID) control remains widely used in industrial control systems, while model predictive control (MPC) is actively studied to achieve hi…
eess.SY2026
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…
eess.SY2025
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…