暂无分享,去创建一个
[1] Biao Huang,et al. System Identification , 2000, Control Theory for Physicists.
[2] Andrew Gordon Wilson,et al. Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints , 2020, NeurIPS.
[3] Jan Peters,et al. Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning , 2019, ICLR.
[4] David Duvenaud,et al. Neural Ordinary Differential Equations , 2018, NeurIPS.
[5] Stefano Stramigioli,et al. Port-Hamiltonian Passivity-Based Control on SE(3) of a Fully Actuated UAV for Aerial Physical Interaction Near-Hovering , 2019, IEEE Robotics and Automation Letters.
[6] J. Webster,et al. Wiley Encyclopedia of Electrical and Electronics Engineering , 2010 .
[7] Jason Yosinski,et al. Hamiltonian Neural Networks , 2019, NeurIPS.
[8] Ioannis G. Kevrekidis,et al. On learning Hamiltonian systems from data. , 2019, Chaos.
[9] Carl E. Rasmussen,et al. PILCO: A Model-Based and Data-Efficient Approach to Policy Search , 2011, ICML.
[10] Rose Yu,et al. Incorporating Symmetry into Deep Dynamics Models for Improved Generalization , 2020, ArXiv.
[11] Arjan van der Schaft,et al. Port-Hamiltonian Systems Theory: An Introductory Overview , 2014, Found. Trends Syst. Control..
[12] E. Hairer,et al. Simulating Hamiltonian dynamics , 2006, Math. Comput..
[13] Hamiltonian formalism for Euler parameters , 1985 .
[14] Davide Scaramuzza,et al. A Benchmark Comparison of Monocular Visual-Inertial Odometry Algorithms for Flying Robots , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).
[15] Johann Reger,et al. IDA-PBC for Underactuated Mechanical Systems in Implicit Port-Hamiltonian Representation , 2019, 2019 18th European Control Conference (ECC).
[17] Kim D. Listmann,et al. Deep Lagrangian Networks for end-to-end learning of energy-based control for under-actuated systems , 2019, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[18] Taeyoung Lee,et al. Geometric tracking control of a quadrotor UAV on SE(3) , 2010, 49th IEEE Conference on Decision and Control (CDC).
[19] Taeyoung,et al. Global Formulations of Lagrangian and Hamiltonian Dynamics on Manifolds , 2017 .
[20] Aníbal Ollero,et al. Robust control of underactuated Aerial Manipulators via IDA-PBC , 2014, 53rd IEEE Conference on Decision and Control.
[21] A. G. Greenhill. Analytical Mechanics , 1890, Nature.
[22] Romeo Ortega,et al. Stabilization of a class of underactuated mechanical systems via interconnection and damping assignment , 2002, IEEE Trans. Autom. Control..
[23] Amit Chakraborty,et al. Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control , 2020, ICLR.
[24] Eric P. Fahrenthold,et al. Hamilton's Equations with Euler Parameters for Rigid Body Dynamics Modeling. Chapter 3 , 2004 .
[25] Davide Scaramuzza,et al. AutoTune: Controller Tuning for High-Speed Flight , 2021, ArXiv.
[26] Amit Chakraborty,et al. Dissipative SymODEN: Encoding Hamiltonian Dynamics with Dissipation and Control into Deep Learning , 2020, ArXiv.
[27] Jan Peters,et al. Model learning for robot control: a survey , 2011, Cognitive Processing.
[28] Guilherme V. Raffo,et al. Passivity Based Control of a Quadrotor UAV , 2014 .
[29] Kevin M. Lynch,et al. Modern Robotics: Mechanics, Planning, and Control , 2017 .
[30] Eldad Haber,et al. Deep Neural Networks Motivated by Partial Differential Equations , 2018, Journal of Mathematical Imaging and Vision.
[31] Mykel J. Kochenderfer,et al. A General Framework for Structured Learning of Mechanical Systems , 2019, ArXiv.
[32] Vijay Kumar,et al. Minimum snap trajectory generation and control for quadrotors , 2011, 2011 IEEE International Conference on Robotics and Automation.
[33] Peter Goldsmith,et al. Modified energy-balancing-based control for the tracking problem , 2008 .
[34] Jianyu Zhang,et al. Symplectic Recurrent Neural Networks , 2020, ICLR.
[35] Thomas A. Runkler,et al. Modeling System Dynamics with Physics-Informed Neural Networks Based on Lagrangian Mechanics , 2020, ArXiv.
[36] Timothy D. Barfoot,et al. State Estimation for Robotics , 2017 .
[37] Vipin Kumar,et al. Integrating Physics-Based Modeling with Machine Learning: A Survey , 2020, ArXiv.
[38] Hannu Tenhunen,et al. A Survey on Odometry for Autonomous Navigation Systems , 2019, IEEE Access.
[39] Andrew Jaegle,et al. Hamiltonian Generative Networks , 2020, ICLR.
[40] Raia Hadsell,et al. Graph networks as learnable physics engines for inference and control , 2018, ICML.
[41] Sergey Levine,et al. Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models , 2018, NeurIPS.
[42] G. Karniadakis,et al. Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems , 2018, 1801.01236.
[43] Miles Cranmer,et al. Lagrangian Neural Networks , 2020, ICLR 2020.
[44] Nolan Wagener,et al. Information theoretic MPC for model-based reinforcement learning , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).
[45] Jean-Marie Souriau,et al. On Geometric Mechanics , 2007 .
[46] Antonio Franchi,et al. Modeling, control and design optimization for a fully-actuated hexarotor aerial vehicle with tilted propellers , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).