Ryozo Nagamune

Ryozo Nagamune

Professor

B.Sc. (Osaka University), M.Sc. (Osaka University), Ph.D. (Royal Institute of Technology Stockholm, Sweden)

phone: (604) 827-4344
email: nagamune@mech.ubc.ca
website:  Control Engineering Laboratory
office: KAIS 3104

Research Interests

Control Engineering:

  • Control of floating offshore wind turbines and wind farms
  • Control of integrated solar thermal systems
  • Control of directed energy deposition metal additive manufacturing processes
  • Control of engine aftertreatment systems
  • Data-driven modeling and control of dynamical systems

Current Research Work

    • Control of floating offshore wind turbines and wind farms: The research objective is to develop control methods to maximize the efficiency of modern large-scale floating offshore wind turbines and wind farms, in order to reduce the levelized cost of energy for offshore wind and to increase the wind energy usage. We are particularly focusing on real-time repositioning of turbines on movable semi-submersible platforms to dynamically modify the wind farm layout so that turbines in the wind farm operate in an intelligent and cooperative manner and the wind farm’s total power capture is maximized for varying wind and wave conditions. This research is supported by NSERC and MITACS.
    • Control of integrated solar thermal systems: The research objective is to develop control methods to optimize the operation of an integrated solar thermal system. The system consists of solar collector tubes as the thermal energy harvester, a heat pump as an auxiliary energy source, and a hot water tank as a thermal energy storage, located at UBC Centre for Interactive Research on Sustainability (CIRS) rooftop. The optimization takes into account the minimization to rely on the heat pump which requires electricity consumption, varying but predictable user’s hot water usage and solar irradiation. This work has been in collaboration with BC-based company Ascent Systems Technologies, and supported by NSERC.
    • Control of directed energy deposition metal additive manufacturing processes: The research objective is to develop control methods to manufacture parts and components with desired mechanical properties for the directed energy deposition (DED) metal additive manufacturing (AM). We are focusing on spatio-temporal temperature control of the deposited parts based on dynamical models of the DED process, and will validate modeling and control methods using a DED AM machine located in UBC Civil Engineering and Mechanical Engineering building. This work is in collaboration with Professor Xiaoliang Jing at UBC Mechanical Engineering.

Selected Publications

      • Niu, Y. and Nagamune, R., “MPC-based multi-objective repositioning control for a floating offshore wind turbine,” to appear in IEEE Transactions on Control Systems Technology.
      • Li, K., Takemura, S., Sencer, B., Nagamune, R. and Kakinuma, Y., “Model-based laser preheating control for high-speed directed energy deposition (DED) pipe coating,” to appear in CIRP Annals Manufacturing Technology.
      • Heiskell, C. and Nagamune, R., “Power maximization for floating offshore wind farms under partial operating conditions with reinforcement learning,” to appear in the Proceedings of 2026 IFAC World Congress.
      • Heiskell, C. and Nagamune, R., “Learning from experts: Serial-refine guided wind farm power maximization for floating offshore wind farms,” to appear in the Proceedings of 10th IEEE Conference on Control Technology and Applications (CCTA), 2026.
      • Li, K., Jin, X. and Nagamune, R., “Model-based product height control in directed energy deposition metal additive manufacturing via deep reinforcement learning,” to appear in the Proceedings of 10th IEEE Conference on Control Technology and Applications (CCTA), February 8, 2026.
      • Hu, L., Hu, J. and Nagamune, R., “Economic model predictive control of a heat pump for an integrated solar thermal system,” to appear in the Proceedings of 10th IEEE Conference on Control Technology and Applications (CCTA), 2026.
      • Li, K., Jin, X. and Nagamune, R., “Reinforcement learning-based feedforward control for solidification cooling rate regulation in directed energy deposition,” 9th IEEE Conference on Control Technology and Applications (CCTA), pp. 906-911, San Diego, CA, USA, August 25-27, 2025.
      • Li, K., Yu, S., Jin, X. and Nagamune, R., “Melt pool area control in directed energy deposition using iterative learning control and substrate pre-heating,” 2025 American Control Conference, pp. 2925-2930, Denver, CO, USA, July 8-10, 2025.
      • Niu, Y. and Nagamune, R., “Power maximization and platform oscillation mitigation in reconfigurable floating offshore wind farms,” 2025 American Control Conference, pp. 1488-1493, Denver, CO, USA, July 8-10, 2025.
      • Li, K., Jin, X. and Nagamune, R., “Adaptive spatiotemporal thermal model for real-time temperature prediction in directed energy deposition,” Procedia CIRP, vol. 137, pp. 362-367, 2025.
      • Li, J., Nagamune, R., Zhang, Y. and Li, S.E., “Robust approximate dynamic programming for nonlinear systems with both model error and external disturbance,” IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 1, pp. 896-910, January 2025.
      • Niu, A. Dwivedi, J. Sathiaraj, P. P. Lathi and R. Nagamune, “Floating offshore wind farm control via turbine repositioning: Unlocking the potential unique to floating offshore wind,” IEEE Control Systems Magazine, vol. 44, no. 5, pp. 106-129, October 2024.
      • Rad and R. Nagamune, “Adaptive energy reference time domain passivity control of haptic interfaces,” IEEE Transactions on Haptics, vol. 17, no. 3, pp. 360-371, July-September 2024.
      • J. Li, R. Nagamune, Y. Zhang and S. E. Li, “Robust approximate dynamic programming for nonlinear systems with both model error and external disturbance,” to appear in IEEE Transactions on Neural Networks and Learning Systems.
      • Y. Zhou, P. Bhowmick, L. Zhang, L. Chen, R. Nagamune and Y. Li, “A model reference adaptive control framework for floating offshore wind turbines with collective and individual blade pitch strategy,” Ocean Engineering, vol. 291, 116054 (11 pages), January 2024.
      • B. Saunders and R. Nagamune, “Fatigue load minimization for a position-controlled floating offshore wind turbine,” Journal of Marine Science and Engineering, vol. 11, no. 20, 2274 (15 pages), 2023.
      • M. Rostam, R. Nagamune and V. Grebenyuk, “Self-tuning kernel Gaussian method for predictive control systems,” Journal of Process Control, vol. 128, 103009 (12 pages), 2023.
      • T. Zhong, W. Tang, R. Nagamune and D. Bao, “Gain-scheduling robust control with guaranteed stability for ball screw drives with uncertain load mass and varying resonant modes,” Precision Engineering, vol. 80, pp. 198-207, March 2023.
      • R. Dong, R. Nagamune and A. Wu, “Anti-unwinding nonsingular terminal sliding mode control with attitude maneuver planning for flexible spacecraft,” International Journal of Robust and Nonlinear Control, vol. 33, no. 3, pp. 2090-2112, February 2023.
      • J. Lim, P. Kirchen and R. Nagamune, “Gain-scheduling selective catalytic reduction control in diesel engines with switched H-infinity controllers,“ International Journal of Automotive Technology, vol. 24, no. 1, pp. 105-113, February 2023.

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