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Performance Improvements in Extremum Seeking Control Performance Improvements in Extremum Seeking Control M. Guay September 30, 2016, LCCC Process Control Worshop, Lund 1 Background 2 Perturbation based ESC Basic perturbation based ESC Proportional-integral ESC 3 Recursive least-squares approach RLS Proportional integral ESC 4 Discrete-time systems 5 Distributed network optimization 6 Concluding R

http://www.lccc.lth.se/media/2016/process-workshop/slides/Guay.pdf - 2025-10-28

LCCC_Talk_2016 Control Systems Engineering Laboratory CSEL LCCC Workshop on Process Control Sept. 28 - 30, 2016 Production-Inventory Systems: Modeling, Forecasting and Control Daniel E. Rivera Control Systems Engineering Laboratory School for the Engineering of Matter, Transport and Energy Ira A. Fulton Schools of Engineering Arizona State University http://csel.asu.edu Control Systems Engineering

http://www.lccc.lth.se/media/2016/process-workshop/slides/LCCC_Talk_2016_Rivera.pdf - 2025-10-28

UTC Slides Control Design and Verification with Physics Based Models for HVAC/R Applications Junqiang (James) Fan Fellow, Systems and Controls Engineering Sept 28, 2016 OUTLINE Vapor compression refrigeration cycle Model Based Control Development Process Application Examples Transportation Refrigeration Commercial Refrigeration Residential HVAC Commercial Building HVAC Conclusions 2 VAPOR

http://www.lccc.lth.se/media/2016/process-workshop/slides/MBD_HVACR_control_LCCCTalk_JamesFan.pdf - 2025-10-28

PowerPoint-Präsentation This project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 611281. Optimal resource allocation in industrial complexes by distributed optimization and dynamic pricing 1Process Dynamics and Operations Group, Department of Biochemical and Chemical Engineering TU Dor

http://www.lccc.lth.se/media/2016/process-workshop/slides/Slides_Lund_2016_Engell.pdf - 2025-10-28

A simple multi-agent consensus model with state-dependent interaction topology Agents with state-dependent interactions Vincent Blondel UCLouvain, Belgium (joint work with John Tsitsiklis and Julien Hendrickx, MIT) May 2009 Lund University IEEE Transaction on Automatic Control, 2009 xi(t+ 1) = X j:(i,j)∈E aijxj(t) x1 x6 x5 x4 x3 x2 x1 x6 x5 x4 x3 x2 ẋi(t) = X j:(i,j)∈E (xj(t)− xi(t)) x1 x6 x5 x4

http://www.lccc.lth.se/media/LCCC2009/workshopmaj2009/2009lund-blondel.pdf - 2025-10-28

Suboptimality estimates for NMPC schemes Lars Grüne Mathematisches Institut, Universität Bayreuth in collaboration with Anders Rantzer (Lund), Jürgen Pannek, Karl Worthmann (Bayreuth) supported by DFG priority research program 1305 “Control theory for digitally networked dynamical systems” First LCCC workshop, Lund, May 28-29, 2009 Setup We consider nonlinear discrete time control systems x(n +

http://www.lccc.lth.se/media/LCCC2009/workshopmaj2009/2009lund-gruene.pdf - 2025-10-28

1 The Price of Anarchy: Some Old and New Results Tim Roughgarden Stanford University 2 Algorithms and Game Theory Recent Trend: design and analysis of algorithms and systems with self-interested agents Motivation: the Internet •  auctions (eBay, sponsored search, etc.) •  competition among end users, ISPs, etc. Traditional approach: •  agents classified as obedient or adversarial –  examples: dist

http://www.lccc.lth.se/media/LCCC2009/workshopmaj2009/2009lund-roughgarden.pdf - 2025-10-28

RSS06 * * Controllability of Networked Systems from a Graph-Theoretic Perspective Outline: Graph-Based Control Leader-Networks Controllability Epidemic Programming Magnus Egerstedt GRITSLab Electrical and Computer Engineering Georgia Institute of Technology www.ece.gatech.edu/~magnus Magnus Egerstedt - Spring 2009 Magnus Egerstedt - Spring 2009 The Mandatory Bio-Slide As sensor webs, large-scale r

http://www.lccc.lth.se/media/LCCC2009/workshopmaj2009/Egerstedt2009LCCC.ppt - 2025-10-28

Learning Flexible Goal-Directed Behavior Christian Balkenius Lund University Cognitive Science Wednesday, April 18, 12 http://www.lccc.lth.se/index.php?mact=ReglerSeminars,cntnt01,abstractbio,0&cntnt01workshop=Symp1204&cntnt01abstractID=325&cntnt01origid=142&cntnt01returnid=Abstract http://www.lccc.lth.se/index.php?mact=ReglerSeminars,cntnt01,abstractbio,0&cntnt01workshop=Symp1204&cntnt01abstractI

http://www.lccc.lth.se/media/LCCC2012/SymposiumApril/slides/balkenius.pdf - 2025-10-28

Cognition-Enabled Robot Control for the Realization of Home Chore Task Intelligence Cognition-Enabled Robot Control for the Realization of Home Chore Task Intelligence Michael Beetz Intelligent Autonomous Systems Technische Universität München LCCC & Rosetta Symposium on Robot Skill Learning and Cognition April 18, 2012 Introduction Cognition-enabled Control Housework Principles Everyday Robot M

http://www.lccc.lth.se/media/LCCC2012/SymposiumApril/slides/beetz.pdf - 2025-10-28

Lund_Apr-2012.pptx http://lasa.epfl.ch Programming by Demonstration: Some recent challenges Aude G. Billard LASA Laboratory EPFL http://lasa.epfl.ch Learning a control law that ensures that you reach the target even if perturbed and that you follow a particular dynamics Generalizing: Learning a control law http://lasa.epfl.ch Learn a control law from examples 1x 2x Time-invariant DS with stable at

http://www.lccc.lth.se/media/LCCC2012/SymposiumApril/slides/billard.pdf - 2025-10-28

Abstraction by Structure Introduction Structural Representations Structural Models Conclusion References Abstraction by Structure Carl Henrik Ek, Danica Kragic {chek, danik}@csc.kth.se Royal Institute of Technology April 18, 2012 Ek, Kragic KTH Abstraction by Structure Introduction Structural Representations Structural Models Conclusion ReferencesComputer Vision and Robotics at KTH i) Head of grou

http://www.lccc.lth.se/media/LCCC2012/SymposiumApril/slides/ek.pdf - 2025-10-28

Microsoft PowerPoint - Geminoid2配布.pptx Learning for Humanlike Robots Professor of Department of Systems Innovation, Osaka University Group leader of ATR Hiroshi Ishiguro Laboratory ishiguro@sys.es.osaka-u.ac.jp www.is.sys.es.osaka-u.ac.jp, www.geminoid.jp Learning for Humanlike Robots Professor of Department of Systems Innovation, Osaka University Group leader of ATR Hiroshi Ishiguro Laboratory i

http://www.lccc.lth.se/media/LCCC2012/SymposiumApril/slides/ishiguro.pdf - 2025-10-28

ON HUMAN ACTION Volker Krüger Dept. of Mechanical and Production Engineering Aalborg University vok@m-tech.aau.dk Saturday, April 21, 2012 () mailto:vok@m-tech.aau.dk mailto:vok@m-tech.aau.dk Observation World Model Reproduction/Recognition Cleaning the Kitchen Saturday, April 21, 2012 () Observation World Model Reproduction/Recognition Cleaning the Kitchen What is the action? Saturday, April 21,

http://www.lccc.lth.se/media/LCCC2012/SymposiumApril/slides/kruger.pdf - 2025-10-28

Microsoft PowerPoint - 20120418_LCCC.ppt [Compatibility Mode] Transferring Human Skills to Humanoid Robots Dongheui Lee dhlee@tum.de bDynamic Human-Robot-Interaction for Automation Systems (HRI) Lab Department of Electrical Engineering and Information Technology Technical University of MunichTechnical University of Munich Transferring Human Skills to Humanoid Robots Movements • learning Manipulati

http://www.lccc.lth.se/media/LCCC2012/SymposiumApril/slides/lee.pdf - 2025-10-28

untitled Automation & Robotics Research Institute (ARRI) The University of Texas at Arlington, USA F.L. Lewis, Dan Popa Talk available online at http://ARRI.uta.edu/acs Reinforcement Methods for Autonomous Online Learning of Optimal Robot Behaviors Supported by : NSF - Paul Werbos ARO- Sam Stanton AFOSR- Fariba Fahroo Bristol Robotics Lab, University of Bristol, UK Guido Herrmann  Optimal Control

http://www.lccc.lth.se/media/LCCC2012/SymposiumApril/slides/lewis.pdf - 2025-10-28

Common Knowledge of Industrial Robots - Knowledge and Skill Representation for Industrial Robotics Common Knowledge of Industrial Robots Common Knowledge of Industrial Robots Knowledge and Skill Representation for Industrial Robotics Jacek Malec Robotics and Semantic Systems Department of Computer Science Lund University April 18, 2012 LCCC Workshop, 18.04.2012 1(32) Common Knowledge of Industrial

http://www.lccc.lth.se/media/LCCC2012/SymposiumApril/slides/malec.pdf - 2025-10-28

bruyninckx.pptx 1 Hanyang University Il Hong Suh 2012.4.17 Evolution of Species Bacteria cyanobacteria trilobite fish Amphibia Dinosaur Mammalia Human 3.8 billon y ears 1 billon ye ars 700 million y ears eukaryote 1.8 million years Origin of L anguage Darwinian Creature Skinnerian Creature Popporian Creature Gregorian Creature 542 million y ears Cambrian ex plosion 2 billon ye ars Simple cel ls Co

http://www.lccc.lth.se/media/LCCC2012/SymposiumApril/slides/suh.pdf - 2025-10-28

Slide 1 Challenges in adapting imitation and reinforcement learning to compliant robots Sylvain Calinon Learning & Interaction Group Department of Advanced Robotics Italian Institute of Technology (IIT) www.programming-by-demonstration.org/learning-and-interaction/ LCCC Symposium on Robotic Skill Learning and Cognition Lund, 17-19 April 2012 Italian Institute of Technology (IIT) • Created in 2003,

http://www.lccc.lth.se/media/LCCC2012/SymposiumApril/slides/sylvain.pdf - 2025-10-28

movement intelligence before control a story on where to look in biology Patrick van der Smagt bionics und assistive robotics Institute of Robotics and Mechatronics DLR Oberpfaffenhofen biomimetic robotics and machine learning Faculty for Informatics Technische Universität München biology-inspired approach to robotics #1: movement intelligence is caused by superior control (“a robot with a biologi

http://www.lccc.lth.se/media/LCCC2012/SymposiumApril/slides/vandersmagt.pdf - 2025-10-28