Making complex real-world dynamical systems sustainable requires multivariable control that handles multiple input/output and state variables, together with optimization based on real-machine data. Targeting industrial multi-axis motion systems and energy management systems, we work consistently from the theoretical development of control methods based on structural modeling and data-driven optimization through to their experimental validation. Our goal is to develop control and optimization theories and methodologies that can be commonly applied across systems in different fields.

Pushing beyond the limits of the sustainability trade-offs requires system design that spans multiple sectors, together with coordination among multiple stakeholders that accounts for the constraints of each sector. We conduct research toward building cross-sectoral systems that use energy as a common axis, spanning fields such as electric power, transportation, logistics, communications, and data centers. In particular, we are engaged in demonstrative research in collaboration with industry, including demand response that coordinates electric vehicles with electricity markets.

In parallel with promoting electrification for the effective utilization of renewable energy, it is also essential to decarbonize fuels and resources in areas that are difficult to electrify, such as heat demand and chemical feedstocks. We conduct research toward the social implementation of energy technologies, including techno-economic analysis centered on the simultaneous optimization of economic viability, environmental performance, and supply stability in low-carbon hydrogen production that leverages renewable energy and electricity markets. Based on the insights gained, we also aim to contribute to policy recommendations and institutional design.