ep8 - Anuradha Annaswamy: Adaptive Control - From the "Brave Era" to Reinforcement Learning and Back
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01/16/23 • 64 min
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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
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.
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
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.
Previous Episode
ep7 - Jean-Jacques Slotine: Sliding, nonlinear and adaptive control, contraction theory, complex networks, optimization, and machine learning
In this episode, our guest is Jean-Jacques Slotine, Professor of Mechanical Engineering and Information Sciences as well as Brain and Cognitive Sciences, Director of the Nonlinear Systems Laboratory at the Massachusetts Institute of Technology, and Distinguished Faculty at Google AI. We explore and connect a wide range of ideas from nonlinear and adaptive control to robotics, neuroscience, complex networks, optimization and machine learning.
Outline
00:00 - Intro
00:50 - Jean-Jacques' early life
06:17 - Why control?
09:45 - Sliding control and adaptive nonlinear control
18:47 - Neural networks
23:15 - First ventures in neuroscience
28:27 - Contraction theory and applications
48:26 - Synchronization
51:10 - Complex networks
57:59 - Optimization and machine learning
1:08:17 - Advice to future students and outro
Episode links
NCCR Symposium: https://tinyurl.com/bdz84p4c
Sliding mode control: https://tinyurl.com/2s45ra4m
Applied nonlinear control: https://tinyurl.com/4wmbt4bw
On the Adaptive Control of Robot Manipulators: https://tinyurl.com/b7jcpkzw
Gaussian Networks for Direct Adaptive Control: https://tinyurl.com/22zb7pkx
The intermediate cerebellum may function as a wave-variable processor: https://tinyurl.com/2c34ytep
On contraction analysis for nonlinear systems: https://tinyurl.com/5cw4z9j8
Kalman conjecture: https://tinyurl.com/2pfjsbke
I. Prigogine: https://tinyurl.com/5ct8yssb
RNNs of RNNs: https://tinyurl.com/3mpt7fec
How Synchronization Protects from Noise: https://tinyurl.com/2p82erwp
Controllability of complex networks: https://tinyurl.com/24w7hdae
B. Anderson: https://tinyurl.com/e9pkyxdx
Online lectures on nonlinear control: https://tinyurl.com/525cnru4
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.
Next Episode
ep9 - Rodolphe Sepulchre: Spiking control systems, nonlinear control, neuroscience and optimization on manifolds
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
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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