- H. Tsukamoto, J. Hajar, S.-J. Chung, and F. Y. Hadaegh, “Regret-optimal defense against stealthy adversaries: A system level approach,” under review, IEEE Conference on Decision and Control, Mar. 2024.
- H. Tsukamoto, J. D. Ibrahim, J. Hajar, J. Ragan, A. Rahmani, S.-J. Chung, and F. Y. Hadaegh, “Robust optimal network switching topology for zero dynamics attacks,” under review, IEEE Conference on Decision and Control, Mar. 2024.
- H. Tsukamoto, B. Riviere, C. Choi, A. Rahmani, and S.-J. Chung, “CaRT: Certified safety and robust tracking in learning-based motion planning for multi-agent systems,” IEEE Conference on Decision and Control, Dec. 2023.
- H. Tsukamoto, “Contraction theory for robust learning-based control: Toward aerospace and robotic autonomy,” Doctoral Dissertation, Caltech (Best Dissertation Award in Space Engineering), Jun. 2023.
- B. Donitz, D. Mages, H. Tsukamoto, P. Dixon, D. Landau, S.-J. Chung, E. Bufanda, M. Ingham, J. Castillo-Rogez, “Interstellar object accessibility and mission design,” IEEE Aerospace Conference, Mar. 2023.
- H. Tsukamoto, S.-J. Chung, B. Donitz, M. Ingham, D. Mages, and Y. K. Nakka, “Neural-Rendezvous: Learning-based robust guidance and control to encounter interstellar objects,” minor revision under review, AIAA Journal of Guidance, Control, and Dynamics, 2022.
- H. Tsukamoto and S.-J. Chung, “Learning-based robust motion planning with guaranteed stability: A contraction theory approach,” IEEE Robotics and Automation Letters, Oct. 2021 (peer-reviewed and accepted also in IEEE International Conference on Intelligent Robots and Systems, Sep. 2021).
- H. Tsukamoto, S.-J. Chung, and J.-J. E. Slotine, “Learning-based adaptive control using contraction theory,” IEEE Conference on Decision and Control, Dec. 2021.
- S. Singh, H. Tsukamoto, B. Lopez, S.-J. Chung, and J.-J. E. Slotine, “Safe motion planning with tubes and contraction metrics,” IEEE Conference on Decision and Control, Dec. 2021(featured at IEEE CDC tutorial session).
- H. Tsukamoto, S.-J. Chung, J.-J. E. Slotine, and C. Fan, “A theoretical overview of neural contraction metrics for learning-based control with guaranteed stability,” IEEE Conference on Decision and Control, Dec. 2021 (featured at IEEE CDC tutorial session).
- H. Tsukamoto, S.-J. Chung, and J.-J. E. Slotine, “Contraction theory for nonlinear stability analysis and learning-based control: A tutorial overview,” Annual Reviews in Control, Nov. 2021 (tutorial paper invited by Editor-in-Chief).
- H. Tsukamoto, S.-J. Chung and J.-J. E. Slotine, “Neural stochastic contraction metrics for learning-based control and estimation,” IEEE Control Systems Letters, Nov. 2021 (peer-reviewed and accepted also in American Control Conference, May 2021).
- H. Tsukamoto and S.-J. Chung, “Robust controller design for stochastic nonlinear systems via convex optimization,” IEEE Transactions on Automatic Control, Oct. 2021.
- H. Tsukamoto and S.-J. Chung, “Neural contraction metrics for robust estimation and control: A convex optimization approach,” IEEE Control Systems Letters, Jan. 2021 (peer-reviewed and accepted also in IEEE Conference on Decision and Control, Dec. 2020).
- H. Tsukamoto and S.-J. Chung, “Convex optimization-based controller design for stochastic nonlinear systems using contraction analysis,” IEEE Conference on Decision and Control, Dec. 2019.
