ep41 - A minimal history of optimal control

Outline00:00 - Intro08:35 - What is PID control?12:15 - The history: Minorsky, 1922, and steering ships21:00 - The lambda method, IMC, and analytical tuning28:45 - Set-point weighting and two degrees of freedom32:50 - Automatic tuning and the relay feedback problem40:35 - The Lund school and Karl Johan Åström50:20 - PID is nearly optimal57:30 - PID tuning: the elephant in the room1:01:35 - Open problems and the one-third rule1:05:45 - Feedforward: the unsung hero1:15:00 - PID as a building block in large-scale control1:21:40 - Teaching, outsourcing, and lost knowledge1:28:15 - PID in the AI age1:32:25 - Microalgae and sustainable process control1:40:05 - Advice for young researchers and the future of PIDLinksHägglund & Guzmán, "Give us PID controllers and we can control the world": https://doi.org/10.1016/j.ifacol.2024.08.018Minorsky, "Directional Stability of Automatically Steered Bodies": https://doi.org/10.1111/j.1559-3584.1922.tb04958.xHazen, "Theory of Servo-Mechanisms": https://doi.org/10.1016/S0016-0032(34)90254-4Ziegler & Nichols, "Optimum Settings for Automatic Controllers": https://doi.org/10.1115/1.4019264Dahlin, "Designing and Tuning Digital Controllers" (the lambda method): https://skoge.folk.ntnu.no/puublications_others/1968_Dahlin%20-%20Designing%20and%20tuning%20digital%20controllers.pdfRivera, Morari & Skogestad, "Internal Model Control: PID Controller Design": https://skoge.folk.ntnu.no/publications/1986/Rivera86/Rivera86.pdfÅström & Hägglund, "Automatic Tuning of Simple Regulators": https://cse.lab.imtlucca.it/~bemporad/teaching/controllodigitale/pdf/Astrom-ACC89.pdfHägglund & Åström, "Industrial Adaptive Controllers Based on Frequency Response Techniques": https://doi.org/10.1016/0005-1098(91)90052-4Åström & Hägglund, "The Future of PID Control": https://doi.org/10.1016/S0967-0661(01)00062-4Hägglund, "A Unified Discussion on Signal Filtering in PID Control": https://doi.org/10.1016/j.conengprac.2013.03.012Theorin & Hägglund, "Derivative Backoff: The Other Saturation Problem": https://doi.org/10.1016/j.jprocont.2015.06.008Hägglund, mid-ranging control (2021): https://skoge.folk.ntnu.no/puublications_others/apc-papers/midranging-control-vpc-hagglund-2020.pdfGuzmán & Hägglund, "Simple Tuning Rules for Feedforward Compensators": https://doi.org/10.1016/j.jprocont.2010.10.007Guzmán, Hägglund, Veronesi & Visioli, "Performance Indices for Feedforward Control": https://doi.org/10.1016/j.jprocont.2014.12.004Del Hoyo, Hägglund, Guzmán & Moreno: https://doi.org/10.1016/j.conengprac.2023.105636Pawlowski, Guzmán, Normey-Rico & Berenguel, "Improving Feedforward Disturbance Compensation in GPC": Improving feedforward disturbance compensation capabilities in Generalized Predictive ControlGuzmán & Hägglund, "Feedforward Control: Analysis, Design, Tuning Rules, and Implementation": https://books.google.it/books/about/Feedforward_Control.html?id=f_C00AEACAAJNormey-Rico & Guzmán, "Unified PID Tuning Approach for Dead-Time Processes": https://doi.org/10.3182/20120328-3-IT-3014.00006Soltesz & Cervin, "When Is PID a Good Choice?": https://doi.org/10.1016/j.ifacol.2018.06.074Grimholt, PID optimality: https://skoge.folk.ntnu.no/publications/thesis/2018_grimholt/phd-thesis-grimholt-2018.pdfHägglund & Guzmán, "Development of Basic Process Control Structures": https://doi.org/10.1016/j.ifacol.2018.06.202Skogestad, "Advanced Control Using Decomposition and Simple Elements": https://doi.org/10.1016/j.arcontrol.2023.100903Guzmán, Hägglund et al., "Understanding PID Design Through Interactive Tools": https://doi.org/10.3182/20140824-6-ZA-1003.01328ISA-TR5.9-2023, "Proportional-Integral-Derivative Control Algorithms and Performance": https://www.isa.org/products/isa-tr5-9-2023-proportional-integral-derivative-piVilanova & Visioli, "PID Control in the Third Millennium": https://books.google.it/books/about/PID_Controllers.html?id=FsyhngEACAAJBrian Douglas — control engineering education videos: https://engineeringmedia.com/videosA Practical Guide to PID Controller Implementation: https://arxiv.org/abs/2604.15918Support 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.

Outline00:00 - Intro02:25 - Bachelier and the Théorie de la Spéculation03:05 - Stochastic processes, Brownian motion, and the heat equation09:45 - Poincaré's verdict, obscurity, and rediscovery13:50 - Robert C. Merton: from hot rods to MIT19:25 - Dynamic programming and Itô calculus24:35 - Merton's portfolio problem as stochastic optimal control31:10 - Options, dynamic hedging, and the Black–Scholes–Merton equation39:50 - LTCM: the dream team46:30 - August 1998: the crash49:00 - Fat tails and the ten-sigma defense51:40 - The ghosts of 2008 and echoes in the AI boom54:00 - Robustness embraced at last: Hansen and Sargent57:45 - OutroLinksBachelier's thesis, "Théorie de la Spéculation" (1900): https://www.numdam.org/item/10.24033/asens.476.pdfCourtault et al., "Louis Bachelier on the Centenary of Théorie de la Spéculation": https://doi.org/10.1111/1467-9965.00098Merton's Nobel autobiography: https://www.nobelprize.org/prizes/economic-sciences/1997/merton/biographical/Merton's MIT "Infinite History" interview: https://infinite.mit.edu/video/robert-c-merton-phd-%E2%80%9970/Mandelbrot, "The Variation of Certain Speculative Prices": https://doi.org/10.1086/294632Merton, "Optimum Consumption and Portfolio Rules in a Continuous-Time Model": https://doi.org/10.1016/0022-0531(71)90038-XMoehle & Boyd, "A Certainty Equivalent Merton Problem": https://doi.org/10.1109/LCSYS.2021.3111534Brigo & Mercurio, "Interest Rate Models: Theory and Practice": https://doi.org/10.1007/978-3-540-34604-3Armstrong, Brigo & Hanzon, "Optimal Projection Filters with Information Geometry": https://doi.org/10.1007/s41884-023-00108-xHu & Zhou, "Constrained Stochastic LQ Control with Random Coefficients, and Application to Portfolio Selection": https://doi.org/10.1137/S0363012904441969Black & Scholes, "The Pricing of Options and Corporate Liabilities": https://doi.org/10.1086/260062Merton, "Theory of Rational Option Pricing": https://doi.org/10.2307/3003143Merton, "Option Pricing When Underlying Stock Returns Are Discontinuous": https://doi.org/10.1016/0304-405X(76)90022-2Scholes' Nobel lecture: https://www.nobelprize.org/prizes/economic-sciences/1997/scholes/lecture/Merton's Nobel lecture: https://www.nobelprize.org/prizes/economic-sciences/1997/merton/lecture/Markowitz, "Portfolio Selection": https://doi.org/10.2307/2975974Michael Lewis, "Liar's Poker": https://en.wikipedia.org/wiki/Liar%27s_PokerEdwards, "Hedge Funds and the Collapse of Long-Term Capital Management": https://doi.org/10.1257/jep.13.2.189Lowenstein, "When Genius Failed": https://en.wikipedia.org/wiki/When_Genius_FailedTaleb, "Statistical Consequences of Fat Tails": https://arxiv.org/abs/2001.10488Taleb & West, "Working with Convex Responses: Antifragility from Finance to Oncology": https://doi.org/10.3390/e25020343Taleb, "The Black Swan": https://en.wikipedia.org/wiki/The_Black_Swan:_The_Impact_of_the_Highly_ImprobableTaleb, "Fooled by Randomness": https://en.wikipedia.org/wiki/Fooled_by_RandomnessMan Group, "The AI Bubble: Hidden Risks and Opportunities": https://www.man.com/insights/the-ai-bubbleSen. Warren's remarks at the Vanderbilt Policy Accelerator: https://www.banking.senate.gov/newsroom/minority/warren-remarks-at-vanderbilt-policy-accelerator-event-highlighting-economic-and-financial-risks-of-potential-ai-crashMeng & Chen, "Artificial Intelligence and Systemic Risk": https://arxiv.org/abs/2604.03272Doyle, "Guaranteed Margins for LQG Regulators": https://doi.org/10.1109/TAC.1978.1101791Safonov & Athans, "Gain and Phase Margin for Multiloop LQG Regulators": https://doi.org/10.1109/TAC.1977.1101470Hansen & Sargent, "Robust Control and Model Uncertainty": https://doi.org/10.1257/aer.91.2.60Hansen & Sargent, "Wanting Robustness in Macroeconomics": http://www.tomsargent.com/research/wanting.pdfSupport 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.

Outline 00:00 - Intro 02:10 - London in the 1960s12:40 - From Oxford to Imperial College: David Mayne and the discrete-time Riccati equation 18:05 - The "global tour": Montenegro roads, hitch-hiking to Istanbul, and the San Francisco waterfront 22:30 - Feedback and causality between stochastic processes 31:15 - The system identification years 40:50 - Model complexity, the bias–variance trade-off, and concentration inequalities 52:05 - Adaptive control: living through a golden era 1:00:30 - McGill, George Zames, and CIFAR's "institute without walls," and COCOLOG 1:09:45 - Mean field games: the China connection, the cell-phone problem, and Nash Certainty Equivalence 1:20:15 - The Lasry–Lions simultaneous discovery 1:24:40 - From graphons to graphexons: sparse networks, Laplexions, and geometry 1:31:00 - Linear Stochastic Systems, Popper, and falsifiability 1:35:20 - Advice to young researchers 1:38:00 - OutroLinks Peter Caines' website: https://www.mcgill.ca/cim/caines Linear Stochastic Systems: https://epubs.siam.org/doi/book/10.1137/1.9781611974713  On the discrete-time matrix Riccati equation of optimal control: https://doi.org/10.1080/00207177008931892 Feedback between stationary stochastic processes: https://doi.org/10.1109/TAC.1975.1101008 Prediction-error identification methods for stationary stochastic processes: https://doi.org/10.1109/TAC.1976.1101304 Asymptotic normality of prediction-error estimators for approximate system models: https://doi.org/10.1109/CDC.1978.268066 Discrete-time multivariable adaptive control (Axelby Award): https://doi.org/10.1109/TAC.1980.1102363 Discrete-time stochastic adaptive control: https://doi.org/10.1137/0319052 25 seminal control papers of the 20th century: https://books.google.ca/books/about/Control_Theory.html?id=eVhGAAAAYAAJ COCOLOG: A conditional observer and controller logic for finite machines: https://epubs.siam.org/doi/10.1137/S0363012992226636 Hierarchical hybrid control systems: https://doi.org/10.1109/9.664153 On the hybrid optimal control problem: https://ieeexplore.ieee.org/document/4303244Bode Lecture: https://ieeecss.org/presentation/bode-lecture/mean-field-stochastic-control The cell-phone problem - Large population stochastic wireless power control: https://doi.org/10.1109/CDC.2003.1272542 Large-population stochastic dynamic games - McKean-Vlasov and the Nash Certainty Equivalence principle: https://projecteuclid.org/journals/communications-in-information-and-systems/volume-6/issue-3/Large-population-stochastic-dynamic-games--closed-loop-McKean-Vlasov/cis/1183728987.full Large-population cost-coupled LQG with nonuniform agents and decentralized ε-Nash equilibria: https://doi.org/10.1109/TAC.2007.904450 Social optima in mean field LQG control: https://doi.org/10.1109/TAC.2012.2183439 ε-Nash mean field games with major and minor agents: https://arxiv.org/abs/1209.5684 Graphon mean field games and their equations: https://doi.org/10.1137/20M136373X Mean field games on large sparse network limits - Laplexion dynamics on graphexons: https://www.sciencedirect.com/science/article/pii/S240589632500388X Murray Wonham oral history: https://www.youtube.com/watch?v=8IBZyRo0vDkSupport 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.

Outline00:00 - 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 systems26:55 - Complex networks, synchronization, and pinning control 40:30- Synthetic biology: from gene regulatory networks to multicellular control58:00 - 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 - OutroLinks 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_chaosErasmus 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/S0218127496000254Piecewise-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.2008.07.007Controllability of complex networks: https://doi.org/10.1038/nature10011Controlling complex networks with complex nodes: https://doi.org/10.1038/s42254-023-00566-3Analysis, design and implementation of a novel scheme for in-vivo control of synthetic gene regulatory networks: https://doi.org/10.1016/j.automatica.2011.01.073In-vivo Real-time Control of Protein Expression from Endogenous and Synthetic Gene Networks: https://doi.org/10.1371/journal.pcbi.1003625A network model of Italy shows that intermittent regional strategies can alleviate the COVID-19 epidemic: https://doi.org/10.1038/s41467-020-18827-5A Continuification-Based Control Solution for Large-Scale Shepherding: https://arxiv.org/abs/2411.04791Shepherding control and herdability in complex multiagent systems: https://doi.org/10.1103/PhysRevResearch.6.L032012Nonreciprocal field theory for decision-making in multi-agent control systems: https://doi.org/10.1038/s41467-025-63071-4Support 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.

Outline00:00 - Intro01:15 - 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 - OutroLinksSteve'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/PyDMDDiscovering 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.2018.0335PySINDy: https://github.com/dynamicslab/pysindy SINDy with control: https://arxiv.org/abs/2108.13404 SINDy review: https://doi.org/10.1146/annurev-control-030123-015238Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control: http://www.databookuw.comExplainable AI: Learning from the Learners: https://arxiv.org/abs/2601.05525HydroGym: https://github.com/dynamicslab/hydrogymSupport 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.

Outline00:00 – Intro04:43 – Life and background08:45 – Bell Labs13:42 – Inventing the negative feedback amplifier18:15 – Nyquist's landmark contributions20:43 – Regeneration theory27:10 – Frequency response32:03 – Cauchy’s argument principle36:05 – The Nyquist criterion41:37 – Why is it so hard?45:27 – Robustness, margins, and practical aspects56:41 – Beyond the Nyquist criterion1:04:25 – Pitfalls and common misunderstandings1:07:00 – OutroLinksBrian Douglas's video: http://y2u.be/sof3meN96MAThe Idea Factory: https://en.wikipedia.org/wiki/The_Idea_FactoryInventing the Negative Feedback Amplifier: https://doi.org/10.1109/MSPEC.1977.6501721Johnson–Nyquist noise:  https://doi.org/10.1103/PhysRev.32.110Nyquist sampling theorem: https://en.wikipedia.org/wiki/Nyquist%E2%80%93Shannon_sampling_theoremRegeneration theory: https://doi.org/10.1002/j.1538-7305.1932.tb02344.xGain and phase margins: https://en.wikipedia.org/wiki/Bode_plot#Gain_margin_and_phase_marginRouth–Hurwitz criterion: https://en.wikipedia.org/wiki/Routh%E2%80%93Hurwitz_stability_criterionÅström’s lecture: https://archive.control.lth.se/media/Staff/KarlJohanAstrom/Lectures/ASMENyquistLecture2005.pdfScale-Relative Graphs: https://doi.org/10.1109/TAC.2023.3234016Support 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.

Outline00:00 - Intro02:55 - Brachistochrone problem20:52 - Beginning of the calculus of variations32:00 - Principle of least action42:37 - Maximum principle1:02:35 - Dynamic programming1:11:12 - Linear quadratic control1:16:37 - Beyond optimal control: games, nonsmooth analysis, MPC, RL1:28:40 - OutroLinks300 years of optimal control: https://tinyurl.com/2s3t8se4Brachistochrone: https://tinyurl.com/mwmv38ewActa Eruditorum, 1696: https://tinyurl.com/55yf5v49Acta Eruditorum, 1697: https://tinyurl.com/2a7msaajBernoulli family: https://tinyurl.com/y2vx2xdnLeibniz–Newton calculus controversy: https://tinyurl.com/3974fdhdCalculus of variations: https://tinyurl.com/3vvz8tufBeginning of the Calculus of Variations: https://tinyurl.com/mv6btxfnLagrangian mechanics: https://tinyurl.com/ycx5fv46Euler–Lagrange equation: https://tinyurl.com/53yybvyxHamiltonian mechanics: https://tinyurl.com/yfrd8zhzHamilton–Jacobi equation: https://tinyurl.com/46m9cuvsPontryagin: https://tinyurl.com/35ehxnexPontryagin’s autobiography:  https://ega-math.narod.ru/LSP/book.htmDiscovery of the Maximum Principle: https://tinyurl.com/3s43nv4tMaximum Principle: https://tinyurl.com/4f7352t4Goddard problem: https://tinyurl.com/5n8swp2mHamilton–Jacobi–Bellman equation: https://tinyurl.com/4uemn5y4Kalman filter: https://tinyurl.com/39zx5yryClarke: https://tinyurl.com/yj2tzcjbMPC: https://tinyurl.com/4sf5pzvy RL: https://tinyurl.com/ee5ne7szAlphaGo: https://tinyurl.com/ydrf8jscSupport 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.

Outline00:00 - Intro03:18 - Early days: why control, M. Athans, and IDSS12:21 - What is gain scheduling?33:37 - Paradigm shifts & the ‘90s: Minnesota → Texas → LA 42:19 - Robustness & fundamental limitations of nonlinear systems57:35 - Set-valued control & estimation01:04:52 - Game theory & multi-agent control 01:28:18 - Learning & dissipativity in games & multi agent AI01:45:03 - KAUST: building something new01:53:33 - On human-algorithmic interaction01:59:07 - Advice to future students: control, jiu-jitsu, and chatbots in education2:10:02 - OutroLinksJeff’s website: https://tinyurl.com/52btmmz7CSM interview: https://tinyurl.com/49wh98x7Domain: feedbackcontrol.comM. Athans: https://tinyurl.com/nhbw66waPhD thesis: https://tinyurl.com/5eyxkfm6IDSS: https://tinyurl.com/bdenwy6dResearch on gain scheduling: https://tinyurl.com/55se8zcr Overview of LPV systems: https://tinyurl.com/3ksff58b Åström’s lecture: https://tinyurl.com/33mxkkfe Necessity of the small gain theorem: https://tinyurl.com/mjn9eeb4 Sensitivity reduction for nonlinear plants: https://tinyurl.com/23tej5ypRespect the unstable: https://tinyurl.com/3yww5eds Differential inclusion: https://tinyurl.com/4yvc8vccLectures on game theory: https://tinyurl.com/4z8hh3rnDynamic fictitious play: https://tinyurl.com/yc6wsxjjCooperative control and potential games: https://tinyurl.com/4hbmrt72Dissipativity theory in game theory: https://tinyurl.com/3theyc7xPopulation games, stable games, and passivity: https://tinyurl.com/zxwtzv6wGame theory and control: https://tinyurl.com/yencrwm3Higher-order uncoupled learning: https://tinyurl.com/376r5r9xSupport 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.

Outline00:00 - Intro05:01 - Female Influencers in Control — The Backstory08:08 - Sofya Kovalevskaya15:21 - Irmgard Lotz26:16 - A new wave of control influencers34:26 - Some data43:38 - What can one do? 1:00:10 - Exhibition + survey1:05:21 - OutroLinksFemale influencers in control project: https://tinyurl.com/mv879ahfCharlotta Johnsson: https://tinyurl.com/343esbeuEva Westin: https://tinyurl.com/3p6fd5n8Margret Bauer: https://tinyurl.com/47d35xzbSofya Kovalevskaya: https://tinyurl.com/4mmzruwcRemembering Sofya Kovalevskaya: https://tinyurl.com/4cpw7vffIrmgard Lotz: https://tinyurl.com/y2exmndmFlow Computation Pioneer Irmgard Flügge-Lotz (1903–1974): https://tinyurl.com/4cy3xsp3Discontinuous Automatic Control: https://tinyurl.com/yeys5dxxHistorical Female Influencers in Automatic Control: https://tinyurl.com/yxw4bjxeActivity report: https://tinyurl.com/jwzn4z3cSupport 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.

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.