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Email: sakurama at sys.es.osaka-u.ac.jp
For international students interested in our lab, please contact the AAD as outlined in the application guidelines.
Control Information Systems Research Group, Area of Theoretical Systems Science, Division of Mathematical Science for Social Systems, Department of Systems Innovation, Graduate School of Engineering Science , Osaka University
Graduate School of Informatics, Kyoto University, Ph.D. 2004
Graduate School of Informatics, Kyoto University, Master’s degree of Informatics 2001
Faculty of Engineering, Kyoto University, Bachelor’s degree of Engineering 1999
Graduate School of Informatics, Kyoto University, Associate Professor 2018–2024
Graduate School of Engineering, Tottori University, Associate Professor 2011–2018
The University of Electro-Communications, Research Associate 2004–2011
Design of autonomous distributed control systems for large-scale systems
Distributed control and distributed sensing of swarm robots and mobile swarms
Control and optimization of social systems (traffic systems and power systems)
Keywords: Distributed control, Distributed matching, Multi-view learning, Swarm robotics, Drone formation, Sensor networks, Cyber-physical systems, Mobility systems
C. Peng, K. Sakurama, N. Miyano, M. Yamazumi, T. Sugawara. Distributed SE(d) formation control of multi-agent systems using relative measurements. International Journal of Robust and Nonlinear Control (Accepted)
K. Sakurama, R. Asai, M. Yamazumi. Multiagent Coordination With Relative Measurement via Cooperative and Individual Control. IEEE Transactions on Automatic Control, Vol. 70, No. 2, pp. 992-1007, February 2025
K. Sakurama, C. Peng, R. Asai, H. Sakata, M. Yamazumi. Formation Control of Nonholonomic Multi-robot Systems over Robot Coordinate Frames and Its Application to LiDAR-based Robots. IEEE Transactions on Control Systems Technology, Vol. 32, No. 6, pp. 2075-2089, November 2024
Y. Watanabe, K. Sakurama, H. Ahn. Gradient-Based Distributed Controller Design Over Directed Networks. IEEE Transactions on Control of Network Systems, Vol. 11, No. 4, pp. 1998-2009, December 2024
K. Sakurama, K. Kashima, T. Ikeda, N. Hayashi, K. Hoshino, M. Ogura, and C. Zhao. System-control-based optimization of one-way car-sharing services. In Advanced Mathematical Science for Mobility Society. Springer. 2024 (Book)
V. H. Pham, K. Sakurama, S. Mou, and H. Ahn. Distributed control for an urban traffic network. IEEE Transactions on Intelligent Transportation Systems, Vol. 23, No. 12, pp. 22937–22953, December 2022
K. Sakurama. Unified formulation of multi-agent coordination with relative measurements. IEEE Transactions on Automatic Control, Vol. 66, No. 9, pp. 4101–4116, September 2021
K. Sakurama and T. Sugie. Generalized coordination of multi-robot systems. Foundations and Trends in Systems and Control, Vol. 9, No. 1, pp. 1–170, 2021 (Book)
K. Sakurama. Control of large-scale cyber-physical systems with agents having various dynamics. IEEE Transactions on Big Data, Vol. 6, No. 4, pp. 691–701, December 2020
K. Sakurama and H. Ahn. Multi-agent coordination over local indexes via clique-based distributed assignment. Automatica, Vol. 112, p. 108670, February 2020
K. Sakurama, S. Azuma, and T. Sugie. Multi-agent coordination via distributed pattern matching. IEEE Transactions on Automatic Control, Vol. 64, No. 8, pp. 3210–3225, August 2019
K. Sakurama, E. I. Verriest, and M. Egerstedt. Scalable stability and time-scale separation of networked, cascaded systems. IEEE Transactions on Control of Network Systems, Vol. 5, No. 1, pp. 321–332, March 2018
K. Wada and K. Sakurama. Privacy masking for distributed optimization and its application to demand response in power grids. IEEE Transactions on Industrial Electronics, Vol. 64, No. 6, pp. 5118–5128, June 2017
K. Sakurama and M. Miura. Communication-based decentralized demand response for smart microgrids. IEEE Transactions on Industrial Electronics, Vol. 64, No. 6, pp. 5192–5202, June 2017
K. Sakurama, S. Azuma, and T. Sugie. Distributed controllers for multi-agent coordination via gradient-flow approach. IEEE Transactions on Automatic Control, Vol. 60, No. 6, pp. 1471–1485, June 2015