inControl

Outline00:00 – Intro07:22 – Anatomy of a feedback loop15:12 – A brief historical recap on the history of feedback23:40 – Inventing the negative feedback amplifier34:28 – Feedback in biology, economics, society, and ... board games!52:44 – Negative vs positive feedback59:15 – Feedback, causality, and the arrow of time1:06:22 – Classics: fundamental limitations, uncertainty, robustness1:21:30 – Adaptive control: learning in the loop1:29:50 – Modern AI feedback loops (RL, social media, alignment)1:40:40 – OutroLinksWatt’s flyball governor: https://tinyurl.com/ne5nene3Maxwell - "On Governors": https://tinyurl.com/2a7cxj7m Black - "Inventing the negative-feedback amplifier": https://tinyurl.com/yevsemdpNyquist Criterion: https://tinyurl.com/33hfbw8mBode's integral: https://tinyurl.com/53sxkdzuWiener - "Cybernetics": https://tinyurl.com/yta899ayApoptosis: https://tinyurl.com/mcxjycka Predator–prey dynamics (Lotka–Volterra): https://tinyurl.com/5cvx33tn Bird migration cues (photoperiodism): https://tinyurl.com/y2e7t22v Neuron action potentials: https://tinyurl.com/2wemwdn4Economic equilibrium & feedback: https://tinyurl.com/nhdx7r3sEcho chambers: https://tinyurl.com/4v8yk7e8 Game design: https://tinyurl.com/bdhdhv38Gap metric (Vinnicombe): https://tinyurl.com/y9nw3yveGeorgiou, Smith - "Feedback Control and the Arrow of Time": https://tinyurl.com/5xvj76jrAnnaswamy, Fradkov - "A Historical Perspective of Adaptive Control and Learning": https://tinyurl.com/4nfew8vm Algorithmic trading flash crash (2010): https://tinyurl.com/2dsrs8j2AI alignment: https://tinyurl.com/yvs3wnj8Support the showPodcast infoPodcast website: https://www.incontrolpodcast.com/Apple Podcasts: https://tinyurl.com/5n84j85jSpotify: https://tinyurl.com/4rwztj3cRSS: https://tinyurl.com/yc2fcv4yYoutube: https://tinyurl.com/bdbvhsj6Facebook: https://tinyurl.com/3z24yr43Twitter: https://twitter.com/IncontrolPInstagram: https://tinyurl.com/35cu4kr4Acknowledgments and sponsorsThis 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.

Dive into the life and legacy of A. M. Lyapunov, exploring his groundbreaking work on stability in motion. Discover how he tackled the challenges of ellipsoidal bodies and developed the direct method using Lyapunov functions. Learn about his contributions to probability, including a proof of the central limit theorem. The podcast also covers his tragic personal life, marked by loss and recognition, culminating in his enduring influence on modern control theory and nonlinear dynamics.

Markos Papageorgiou, Professor Emeritus and pioneer in intelligent transportation systems, shares his insights on the evolution of traffic control. He discusses how traffic control theory has developed from its early days to modern methods, focusing on the dynamic between urban and motorway systems. Markos explains the impact of automation and connectivity on traffic flow, as well as innovative solutions like the Alinea ramp metering system. He also touches upon the future of traffic management, emphasizing the interplay of real-time data and modeling techniques.

Join Cosimo Della Santina, an innovative Associate Professor in Robotics at TU Delft and founder of the Physical Intelligence Lab, as he delves into the fascinating world of soft robotics. He shares insights from his early days with the PISA robotic hand and discusses the complexities of controlling soft robots, emphasizing their natural inspirations. Cosimo also explores the intriguing fusion of robotics and art, alongside the evolution of control systems. His passion for innovation shines through, encouraging future students to embrace unexpected opportunities.

Explore the fascinating world of controllability and observability! Discover how these concepts revolutionized space navigation during the race to the moon. Dive into Kalman's insights on state-space methods and learn about practical tests for engineers. Uncover the applications in complex networks, from optimizing brain stimulation to the challenges of self-driving cars. Plus, hear how these ideas intersect with reinforcement learning in AI. Finally, did you catch the sneak peek into AI-generated voices as a unique observability test?

Outline00:00 - Intro00:42 - “Research should be fun”02:02 - Early steps in research09:00 - Book writing and meeting C. Desoer18:33 - Control synthesis via the factorization approach25:46 - The graph metric 29:27 - Robotics and CAIR36:00 - Randomized algorithms40:41 - On learning44:05 - Neural networks48:40 - Tata, hidden Markov models, and large deviations theory55:48 - Picking problems and role of luck58:07 - Compressed sensing and non-convex optimization01:02:17 - Interplay between control and machine learning01:09:10 - Advice to future students01:13:29 - Future of controlLinksSagar’s website: https://tinyurl.com/4hwruajsHilbert: https://tinyurl.com/ykpdh929Feedback Systems: https://tinyurl.com/2k3jsdatHow to Write Mathematics: https://tinyurl.com/35794bv9Nonlinear systems: https://tinyurl.com/2fdtnjcmC. Desoer: https://tinyurl.com/svhknrenControl Systems Synthesis — A Factorization Approach: https://tinyurl.com/59wdc4svAryabhata: https://tinyurl.com/43x6hfhpA Brief History of the Graph Topology: https://tinyurl.com/49uftzdkRobot Dynamics and Control: https://tinyurl.com/5b4cmt7mCAIR: https://tinyurl.com/rajdtxaxRandomized algorithms for robust controller synthesis using statistical learning theory: https://tinyurl.com/wanpyeucR. Tempo: https://tinyurl.com/jkufdwarVC dimension: https://tinyurl.com/mvwk8afmLearning and Generalisation: https://tinyurl.com/2s3mzh8hAre Analog Neural Networks Better Than Binary Neural Networks? https://tinyurl.com/3fnk27xcHidden Markov Processes: https://tinyurl.com/t5frrvfzAn Introduction to Compressed Sensing: https://tinyurl.com/fc6a8eerSupport the showPodcast infoPodcast website: https://www.incontrolpodcast.com/Apple Podcasts: https://tinyurl.com/5n84j85jSpotify: https://tinyurl.com/4rwztj3cRSS: https://tinyurl.com/yc2fcv4yYoutube: https://tinyurl.com/bdbvhsj6Facebook: https://tinyurl.com/3z24yr43Twitter: https://twitter.com/IncontrolPInstagram: https://tinyurl.com/35cu4kr4Acknowledgments and sponsorsThis 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.

Anders Rantzer, a Professor in Automatic Control at Lund University, dives into his extensive journey through robust control and hybrid systems. He discusses Kharitonov's theorem and integral quadratic constraints, illuminating their significance in stability analysis. Rantzer emphasizes the adaptive nature of control systems and their relevance in real-world applications like energy grids and personalized medicine. His insights on the duality of Lyapunov's theorems showcase the evolution of control theory, while offering valuable advice for students eager to forge connections in the field.

Outline00:00 - Intro01:07 - Early steps02:47 - Why control?05:20 - The move to the US07:40 - The first journal paper13:30 - What is backstepping?17:08 - Grad school25:10 - Stochastic stabilization29:53 - The interest in PDEs43:24 - Navier-Stokes equations52:12 - Hyperbolic PDEs and traffic models57:51 - Predictors for long delays1:08:14 - Extremum seeking1:27:14 - Safe control1:36:30 - Interplay between machine learning and control1:42:28 - Back to the roots: robust adaptive control1:50:50 - On service1:55:54 - AdviceLinksMiroslav’s site: https://flyingv.ucsd.edu/Tuning functions paper: https://tinyurl.com/yznv6r9rP. Kokotović: https://tinyurl.com/mwmbm9yhSeparation and swapping:  https://tinyurl.com/y4fre6t8Adaptive nonlinear stabilizers: https://tinyurl.com/4a9wmmvxKKK book: https://tinyurl.com/2kw2b4k6Stochastic nonlinear stabilization: https://tinyurl.com/4td3537aFollow-up with unknown covariance: https://tinyurl.com/4c4n7fd7Boundary state feedbacks for PIDEs: https://tinyurl.com/4e9y4tdrBoundary Control of PDEs: https://tinyurl.com/d8x38bmjStabilization of Navier–Stokes systems: https://tinyurl.com/4a8cbjemTraffic congestion control: https://tinyurl.com/525jphs5Delay compensation: https://tinyurl.com/5yz6uj9pNonlinear predictors for long delays: https://tinyurl.com/7wvce6vyStability of extremum seeking: https://tinyurl.com/mr5cvzd3Nash equilibrium seeking: https://tinyurl.com/yeywrysnInverse optimal safety filters: https://tinyurl.com/9dkrpvkkNeural operators for PDE control: https://tinyurl.com/5yynsp7vBode lecture: https://tinyurl.com/mp92cs9uCSM article: Support the showPodcast infoPodcast website: https://www.incontrolpodcast.com/Apple Podcasts: https://tinyurl.com/5n84j85jSpotify: https://tinyurl.com/4rwztj3cRSS: https://tinyurl.com/yc2fcv4yYoutube: https://tinyurl.com/bdbvhsj6Facebook: https://tinyurl.com/3z24yr43Twitter: https://twitter.com/IncontrolPInstagram: https://tinyurl.com/35cu4kr4Acknowledgments and sponsorsThis 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.

Manfred Morari, Emeritus Professor at ETH Zurich and a distinguished figure in control theory, shares his insights on the evolution of control systems. He discusses his transformative journey from chemical engineering, emphasizing the importance of blending theory with real-world applications. Morari highlights advancements in PID controller design, robust control, and model predictive control while noting the critical role of collaboration with industry. He also reflects on the integration of AI within control systems, addressing the challenges and opportunities that arise from this intersection.

Explore the remarkable journey of Richard Bellman, the father of dynamic programming, tracing his transition from an early life in Brooklyn to pivotal roles during WWII and at Princeton. Hear about his moral dilemmas at Los Alamos and the RAND Corporation amidst the Cold War. Delve into his groundbreaking contributions to control theory and the curse of dimensionality. Discover the resilience of a man who continued to innovate in mathematics, even after personal challenges, leaving a profound legacy in artificial intelligence and optimization.