Daisuke Tsubakino
Papers
2
Total Citations
7
H-Index
2
About
Daisuke Tsubakino is a control theorist whose research bridges nonlinear systems, robotics, and stochastic control. His work addresses fundamental challenges in ensuring safe and controllable motion under real-world constraints. In his 2015 paper on multiple obstacle avoidance, he developed a framework using nondifferentiable control Lyapunov functions to guide four-wheeled vehicles through cluttered environments while respecting steering limitations—a critical advance for autonomous navigation. This work, with 5 citations, has informed subsequent research in constrained robotic motion planning. Tsubakino has also made significant contributions to controllability theory. His 2019 technical note established a novel sufficient condition for local controllability of single-input nonlinear systems by leveraging deterministic Wiener processes. By allowing control inputs to behave like white noise with unbounded variation, he expanded the theoretical toolkit for analyzing systems that defy traditional smooth control methods. This paper, with 2 citations, demonstrates his ability to merge stochastic analysis with deterministic control theory. His research is characterized by a rigorous mathematical approach to practical problems, from wheeled robots to abstract nonlinear dynamics. Tsubakino’s work continues to influence both control theory and its applications in autonomous systems.
Research Focus
Key Achievements
Top Papers
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