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inControl

著者: Alberto Padoan
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The first podcast on control theory.
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© 2026 inControl
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  • ep44 - Mario di Bernardo: From Circuits to Cells and Swarms — Control meets Complexity
    2026/05/15

    Outline
    00:00 - Intro
    01:30 - Origin story: Naples, electrical engineering, and the fascination with chaos
    08:00 - What is chaos?
    15:00 - DC-DC converters and discontinuity-induced bifurcations
    22:00 - Piecewise-smooth dynamical systems
    26:55 - Complex networks, synchronization, and pinning control
    40:30- Synthetic biology: from gene regulatory networks to multicellular control
    58:00 - COVID-19: a network epidemic model for Italy
    1:02:00 - Multiscale control, statistical mechanics, and physics-informed control
    1:19:10 - State of the field and the IEEE CSS
    1:26:35 - Advice to young researchers
    1:29:00 - Outro

    Links
    Mario's website: https://sites.google.com/site/dibernardogroup/home
    Scuola Superiore Meridionale: https://www.ssm.unina.it/
    Chaos by James Gleick: https://en.wikipedia.org/wiki/Chaos:_Making_a_New_Science
    Control of chaos:https://en.wikipedia.org/wiki/Control_of_chaos
    Erasmus programme: https://en.wikipedia.org/wiki/Erasmus_Programme
    An Adaptive Approach to the Control and Synchronization of Continuous-time Chaotic Systems: https://doi.org/10.1142/S0218127496000254
    Piecewise-smooth Dynamical Systems: Theory and Applications: https://doi.org/10.1007/978-1-84628-708-4
    Bifurcations in nonsmooth dynamical systems: https://doi.org/10.1137/050625060 Controllability of complex networks via pinning:
    https://doi.org/10.1103/PhysRevE.75.046103
    Criteria for global pinning-controllability of complex networks: https://doi.org/10.1016/j.automatica.2008.07.007
    Controllability of complex networks: https://doi.org/10.1038/nature10011
    Controlling complex networks with complex nodes: https://doi.org/10.1038/s42254-023-00566-3
    Analysis, design and implementation of a novel scheme for in-vivo control of synthetic gene regulatory networks: https://doi.org/10.1016/j.automatica.2011.01.073
    In-vivo Real-time Control of Protein Expression from Endogenous and Synthetic Gene Networks: https://doi.org/10.1371/journal.pcbi.1003625
    A network model of Italy shows that intermittent regional strategies can alleviate the COVID-19 epidemic: https://doi.org/10.1038/s41467-020-18827-5
    A Continuification-Based Control Solution for Large-Scale Shepherding:
    https://arxiv.org/abs/2411.04791
    Shepherding control and herdability in complex multiagent systems: https://doi.org/10.1103/PhysRevResearch.6.L032012
    Nonreciprocal field theory for decision-making in multi-agent control systems: https://doi.org/10.1038/s41467-025-63071-4

    Support the show

    Podcast info
    Podcast website: https://www.incontrolpodcast.com/
    Apple Podcasts: https://tinyurl.com/5n84j85j
    Spotify: https://tinyurl.com/4rwztj3c
    RSS: https://tinyurl.com/yc2fcv4y
    Youtube: https://tinyurl.com/bdbvhsj6
    Facebook: https://tinyurl.com/3z24yr43
    Twitter: https://twitter.com/IncontrolP
    Instagram: https://tinyurl.com/35cu4kr4

    Acknowledgments and sponsors
    This episode was supported by the National Centre of Competence in Research on «Dependable, ubiquitous automation» and the IFAC Activity fund. The podcast benefits from the help of an incredibly talented and passionate team. Special thanks to L. Seward, E. Cahard, F. Banis, F. Dörfler, J. Lygeros, ETH studio and mirrorlake . Music was composed by A New Element.

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    1 時間 30 分
  • ep43 - Steve Brunton: DMD, Koopman, SINDy, Eigensteve Channel, HydroGym, Optimization, and much more
    2026/04/15

    Outline
    00:00 - Intro
    01:15 - Origin story: early path and the road to science
    04:20 - On graphical visualization and aphantasia
    08:08 - The interest in fluid dynamics
    12:00 - Caltech, Jerry Marsden, and the move to the Pacific time zone
    19:43 - Dynamic Mode Decomposition (DMD) and the Koopman operator
    27:15 - On teaching and the Eigensteve channel
    39:22 - SINDy: Sparse Identification of Nonlinear Dynamics
    45:45 - Automatic knowledge creation and Explainable AI
    54:31 - HydroGym: RL benchmarks for fluid flow control
    1:01:37 - Optimization boot camp
    1:05:31 - Collimator
    1:13:18 - Outro

    Links
    Steve's website: https://www.eigensteve.com/
    Eigensteve channel: https://www.youtube.com/c/eigensteve
    Jerrold E. Marsden: https://en.wikipedia.org/wiki/Jerrold_E._Marsden
    Aphantasia: https://en.wikipedia.org/wiki/Aphantasia
    J. Nathan Kutz: https://amath.washington.edu/people/j-nathan-kutz
    Clarence W. Rowley: https://cwrowley.princeton.edu/
    DMD: https://en.wikipedia.org/wiki/Dynamic_mode_decomposition
    Koopman operator: https://en.wikipedia.org/wiki/Koopman_operator
    Dynamic Mode Decomposition book: https://epubs.siam.org/doi/book/10.1137/1.9781611974508
    On Dynamic Mode Decomposition paper: https://doi.org/10.3934/jcd.2014.1.391
    DMD with control: https://arxiv.org/abs/1409.6358
    Compressed sensing and DMD: https://doi.org/10.3934/jcd.2015002
    Modern Koopman Theory for Dynamical Systems: https://arxiv.org/abs/2102.12086
    Deep learning for universal linear embeddings of nonlinear dynamics: https://doi.org/10.1038/s41467-018-07210-0
    Data-driven discovery of Koopman eigenfunctions for control: https://doi.org/10.1088/2632-2153/abf0f5
    PyDMD: https://github.com/PyDMD
    Discovering governing equations from data by sparse identification of nonlinear dynamical systems: https://doi.org/10.1073/pnas.1517384113
    Data-driven discovery of partial differential equations:
    https://doi.org/10.1126/sciadv.1602614
    SINDy for model predictive control in the low-data limit:
    https://doi.org/10.1098/rspa.2018.0335
    PySINDy: https://github.com/dynamicslab/pysindy
    SINDy with control: https://arxiv.org/abs/2108.13404
    SINDy review: https://doi.org/10.1146/annurev-control-030123-015238
    Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control: http://www.databookuw.com
    Explainable AI: Learning from the Learners: https://arxiv.org/abs/2601.05525
    HydroGym: https://github.com/dynamicslab/hydrogym

    Support the show

    Podcast info
    Podcast website: https://www.incontrolpodcast.com/
    Apple Podcasts: https://tinyurl.com/5n84j85j
    Spotify: https://tinyurl.com/4rwztj3c
    RSS: https://tinyurl.com/yc2fcv4y
    Youtube: https://tinyurl.com/bdbvhsj6
    Facebook: https://tinyurl.com/3z24yr43
    Twitter: https://twitter.com/IncontrolP
    Instagram: https://tinyurl.com/35cu4kr4

    Acknowledgments and sponsors
    This episode was supported by the National Centre of Competence in Research on «Dependable, ubiquitous automation» and the IFAC Activity fund. The podcast benefits from the help of an incredibly talented and passionate team. Special thanks to L. Seward, E. Cahard, F. Banis, F. Dörfler, J. Lygeros, ETH studio and mirrorlake . Music was composed by A New Element.

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    1 時間 14 分
  • ep42 - inControl guide to ... the Nyquist criterion
    2026/03/16

    Outline

    00:00 – Intro
    04:43 – Life and background
    08:45 – Bell Labs
    13:42 – Inventing the negative feedback amplifier
    18:15 – Nyquist's landmark contributions
    20:43 – Regeneration theory
    27:10 – Frequency response
    32:03 – Cauchy’s argument principle
    36:05 – The Nyquist criterion
    41:37 – Why is it so hard?
    45:27 – Robustness, margins, and practical aspects
    56:41 – Beyond the Nyquist criterion
    1:04:25 – Pitfalls and common misunderstandings
    1:07:00 – Outro

    Links

    Brian Douglas's video: http://y2u.be/sof3meN96MA
    The Idea Factory: https://en.wikipedia.org/wiki/The_Idea_Factory
    Inventing the Negative Feedback Amplifier: https://doi.org/10.1109/MSPEC.1977.6501721
    Johnson–Nyquist noise: https://doi.org/10.1103/PhysRev.32.110
    Nyquist sampling theorem: https://en.wikipedia.org/wiki/Nyquist%E2%80%93Shannon_sampling_theorem
    Regeneration theory: https://doi.org/10.1002/j.1538-7305.1932.tb02344.x
    Gain and phase margins: https://en.wikipedia.org/wiki/Bode_plot#Gain_margin_and_phase_margin
    Routh–Hurwitz criterion: https://en.wikipedia.org/wiki/Routh%E2%80%93Hurwitz_stability_criterion
    Åström’s lecture: https://archive.control.lth.se/media/Staff/KarlJohanAstrom/Lectures/ASMENyquistLecture2005.pdf
    Scale-Relative Graphs: https://doi.org/10.1109/TAC.2023.3234016

    Support the show

    Podcast info
    Podcast website: https://www.incontrolpodcast.com/
    Apple Podcasts: https://tinyurl.com/5n84j85j
    Spotify: https://tinyurl.com/4rwztj3c
    RSS: https://tinyurl.com/yc2fcv4y
    Youtube: https://tinyurl.com/bdbvhsj6
    Facebook: https://tinyurl.com/3z24yr43
    Twitter: https://twitter.com/IncontrolP
    Instagram: https://tinyurl.com/35cu4kr4

    Acknowledgments and sponsors
    This episode was supported by the National Centre of Competence in Research on «Dependable, ubiquitous automation» and the IFAC Activity fund. The podcast benefits from the help of an incredibly talented and passionate team. Special thanks to L. Seward, E. Cahard, F. Banis, F. Dörfler, J. Lygeros, ETH studio and mirrorlake . Music was composed by A New Element.

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    1 時間 10 分
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