ep9 - Rodolphe Sepulchre: Spiking control systems, nonlinear control, neuroscience and optimization on manifolds

02/15/23 • 84 min

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Our guest in this episode is Rodolphe Sepulchre, Professor of Engineering at
KU Leuven in the Deparment of Electrical Engineering (STADIUS) and at the University of Cambridge in the Deparment of Engineering (Control Group). We dive into Rodophe's scientific journey across nonlinear control, neuroscience and optimization on manifolds through the unifying lens of control theory.
Outline
- 00:00 - Intro
- 03:54 - Why control?
- 11:08 - Spiking control systems
- 20:47 - The mixed feedback principle
- 23:52 - On thermodynamics
- 25:17 - Event-based systems
- 29:33 - On dissipativity theory
- 48:00 - Stability, positivity and monotonicity
- 55:00 - Control, cybernetics and neuroscience
- 59:10 - Neuromorphic control principles
- 01:00:01 - Optimization on manifolds
- 01:05:01 - Influential figures
- 01:08:52 - On the future of control
- 01:12:35 - Advice to future students
- 01:15:01 - About creativity
- 01:20:35 - Outro
Episode links
- Rodolphe's lab: https://tinyurl.com/yc4bubyy - IEEE CSM editorials: https://tinyurl.com/2bhch6w3 - Spiking control systems: https://tinyurl.com/3x6pwm9m
- O. Pamuk: https://tinyurl.com/4akzyk37
- Event based control: https://tinyurl.com/5apuh5kw
- A simple neuron servo: https://tinyurl.com/4pjnkx5u
- C. Mead: https://tinyurl.com/mr29xta9
- L. Chua: https://tinyurl.com/5n935ssp
- Inventing the negative feedback amplifier: https://tinyurl.com/4573rv2d
- Hodgkin-Huxley model: https://tinyurl.com/mr46cv79
- R. Ashby: https://tinyurl.com/45jrp6hw
- G. J. Minty: https://tinyurl.com/4u4v22ue
- J. C. Willems: https://tinyurl.com/3zthcxc2
- P. Kokotovic: https://tinyurl.com/mrymffch
- Wholeness and the Implicate Order: https://tinyurl.com/yckpnybp

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Podcast info
Podcast website: https://www.incontrolpodcast.com/
Apple Podcasts: https://tinyurl.com/5n84j85j
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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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Previous Episode

ep8 - Anuradha Annaswamy: Adaptive Control - From the "Brave Era" to Reinforcement Learning and Back

In this episode, our guest is Anuradha Annaswamy. Anu is the Director of the Active-Adaptive Control Laboratory and Senior Research Scientist at the Massachusetts Institute of Technology in the Deparment of Mechanical Engineering. We delve into adaptive control and its exciting history, ranging from the Brave Era to the audacious X15 tests and to modern intersections with Reinforcement Learning.
Outline
02:15 - Anu's background
05:20 - What is adaptation?
08:30 - The Brave Era
15:17 - The X15 accident
23:16 - Exploration vs exploitation
28:35 - Beyond linearity and time invariance
45:05 - Adaptive control vs Reinforcement Learning
52:12 - The future of adaptive control
54:34 - Outro
Episode links
Anu's lab: http://aaclab.mit.edu/NCCR Symposium: https://tinyurl.com/bdz84p4c
Book - Stable adaptive systems: https://tinyurl.com/mw4saame
X-15 Flight 3-65-97: https://tinyurl.com/2kbe7nsy
Paper - Adaptive Control and the NASA X-15-3 Flight Revisited: https://tinyurl.com/2p83k7ez
Paper - A historical perspective of adaptive control and learning: https://tinyurl.com/yck89rcd
Paper -Adaptive Control and Intersections with Reinforcement Learning: https://tinyurl.com/yc27rsyd
KYP Lemma: https://tinyurl.com/mkf35jjt
Persistence of excitation: https://tinyurl.com/bpfwp9n9
Dual control: https://tinyurl.com/ywduzm5x
Paper - Robust adaptive control in the presence of bounded disturbances: https://tinyurl.com/4pztx23z
Paper - Reinforcement learning is direct adaptive optimal control https://tinyurl.com/appnjzyn
MRAC: https://tinyurl.com/bdzzphju
Self Tuning Control: https://tinyurl.com/3mjs3skm

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Next Episode

ep10 - Stephen Boyd: Linear Matrix Inequalities, Convex Optimization, Disciplined Convex Programming, Rock & Roll

In this episode, our guest is Stephen Boyd. Stephen is the Samsung Professor in the School of Engineering at Stanford University. Join as we dive deep into control, convex optimization, linear matrix inequalities, disciplined convex programming, teaching styles, and... rock & roll sound!
Outline
- 00:00 - Intro
- 07:48 - Early years at Berkeley
- 10:25 - The role of theory in practice
- 16:19 - On traveling (intellectually)
- 19:40 - Convex optimization
- 31:51 - On Linear Matrix Inequalities (LMIs)
- 39:57 - Convex Optimization Control Policies (COCPs)
- 50:20 - CVX and Disciplined Convex Programming (DCP)
- 58:14 - About AI
- 1:03:58 - Teaching
- 1:11:07 - Open source and publishing
- 1:15:13 - Future of control and advice to future students
- 1:20:08 - Outro
Episode links
- Stephen’s website: https://tinyurl.com/yrmk6p2w
- CSM acceptance speech: https://tinyurl.com/43yhs583
- L. Chua: https://tinyurl.com/k4zx4vya
- C. Desoer: https://tinyurl.com/4euxvcxx
- S. Sastry: https://tinyurl.com/2p9hfrha
- G. Dantzig: https://tinyurl.com/2s4m3jvz
- Simplex algorithm: https://tinyurl.com/2r8bxwe5
- Interior point methods: https://tinyurl.com/4ev4z6zm
- Invariants and dissipated quantities: https://tinyurl.com/43zswmwt
- Linear matrix inequalities: https://tinyurl.com/4y57date
- COCP paper: https://tinyurl.com/468apvdx
- Keynote talk at L4DC: https://tinyurl.com/2y3z4v68
- Model Predictive Control (MPC): https://tinyurl.com/bdf8r2sx
- DCP: https://tinyurl.com/yc38kvae
- YALMIP: https://tinyurl.com/mr3rk2r4
- Stephen's books: https://tinyurl.com/52v9fu83