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Hiroyasu Tsukamoto
Hiroyasu Tsukamoto
Ph.D. candidate in Space Engineering, California Institute of Technology
Dirección de correo verificada de caltech.edu - Página principal
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Citado por
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Año
Neural Contraction Metrics for Robust Estimation and Control: A Convex Optimization Approach
H Tsukamoto, SJ Chung
IEEE Control Systems Letters (L-CSS) 5 (1), pp. 211-216, 2021
422021
Contraction theory for nonlinear stability analysis and learning-based control: A tutorial overview
H Tsukamoto, SJ Chung, JJE Slotine
Annual Reviews in Control 52, 135-169, 2021
342021
Robust Controller Design for Stochastic Nonlinear Systems via Convex Optimization
H Tsukamoto, SJ Chung
IEEE Transactions on Automatic Control (Early Access), 2020
342020
Neural Stochastic Contraction Metrics for Learning-based Control and Estimation
H Tsukamoto, SJ Chung, JJE Slotine
IEEE Control Systems Letters (L-CSS), Preprint Version, 2020
302020
Learning-based robust motion planning with guaranteed stability: A contraction theory approach
H Tsukamoto, SJ Chung
IEEE Robotics and Automation Letters 6 (4), 6164-6171, 2021
142021
Convex Optimization-based Controller Design for Stochastic Nonlinear Systems using Contraction Analysis
H Tsukamoto, SJ Chung
58th IEEE Conference on Decision and Control (CDC), pp. 8196–8203, 2019
122019
Learning-based adaptive control via contraction theory
H Tsukamoto, SJ Chung, JJ Slotine
IEEE CDC, 2021
92021
Safe motion planning with tubes and contraction metrics
S Singh, H Tsukamoto, BT Lopez, SJ Chung, JJ Slotine
2021 60th IEEE Conference on Decision and Control (CDC), 2943-2948, 2021
62021
A theoretical overview of neural contraction metrics for learning-based control with guaranteed stability
H Tsukamoto, SJ Chung, JJ Slotine, C Fan
2021 60th IEEE Conference on Decision and Control (CDC), 2949-2954, 2021
52021
Neural-Rendezvous: Learning-based Robust Guidance and Control to Encounter Interstellar Objects
H Tsukamoto, SJ Chung, B Donitz, M Ingham, D Mages, YK Nakka
arXiv preprint arXiv:2208.04883, 2022
22022
Learning-based adaptive control using contraction theory
H Tsukamoto, SJ Chung, JJ Slotine
2021 60th IEEE Conference on Decision and Control (CDC), 2533-2538, 2021
22021
Interstellar Object Accessibility and Mission Design
BPS Donitz, D Mages, H Tsukamoto, P Dixon, D Landau, SJ Chung, ...
arXiv preprint arXiv:2210.14980, 2022
12022
Imitation Learning for Robust and Safe Real-time Motion Planning: A Contraction Theory Approach.
H Tsukamoto, SJ Chung
CoRR, 2021
2021
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Artículos 1–13