Differential Algebraic Equations Contents: 1. Introduction 2. Differential Algebraic Equations 3. Linear DAE 4. The Notion of Index 5. Numerical Methods 6. Summary Goal: ◮ To develop a basic understanding of differential-algebraic equations Introduction ◮ Cut a system into subsystems ◮ Use object orientation to structure the system ◮ Write mass, momentum and energy balances for each subsystem ◮ As

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Mechanical Systems 1. Introduction 2. Astronomy 3. Newton, Lagrange, Hamilton and Jacobi 4. Pendulum on a Cart 5. Furuta Pendulum 6. Ball and Beam 7. Summary Natural Science and Engineering Science Many similarities but also many differences Natural Phenomena Insight Understanding Analysis Isolation Fundamental Laws Technical Systems Insight Understanding Synthesis Interaction System Principles Fe

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Friction Models and Friction Compensation Karl J. Åström Department of Automatic Control Lund University 1. Introduction 2. Friction Models 3. The LuGre Model 4. Effects of Friction on Control Systems 5. Friction Compensation 6. Summary Introduction ◮ Essential in Motion Control ◮ Classics Leonardo da Vinci (1452-1519 Amontons 1699 Coulomb 1785 ◮ Tribology ◮ Control ◮ Physics AFM ◮ Surface force a

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Bicycle Dynamics and Control Karl Johan Åström Department of Automatic Control LTH, Lund University Thanks to Richard Klein and Anders Lennartsson Why Model? ◮ Insight and understanding ◮ Analysis, Simulation, Virtual reality ◮ Design optimization ◮ Control design ◮ Implementation The internal model principle A process model is part of the controlller ◮ Operator training ◮ Hardware in the loop sim

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MTK for control Physical modeling in Julia For those about to control Acknowledgement This presentation contains an assortment of content contributed by multiple people ● Chris Rackauckas ● Yingbo Ma ● Probably more, thank you! Outline ● X Differential equations ● Equation-based modeling ○ Symbolics ○ ModelingToolkit (MTK) ○ Tools on top of MTK ● MTK Standard library ● Current status ● Project ide

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Optimal Control of RLCT Networks Circuit Theory Richard Pates Who cares? ...classical theory of passive network synthesis–a beautiful subject that reached its zenith around 1960, only to decline steadily thereafter as an active research interest... –Malcolm Smith Who cares? ...classical theory of passive network synthesis–a beautiful subject that reached its zenith around 1960, only to decline ste

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PowerPoint Presentation Model-Based Policy Learning CS 285: Deep Reinforcement Learning, Decision Making, and Control Sergey Levine Class Notes 1. Homework 3 is out! Due next week • Start early, this one will take a bit longer! 1. Last time: model-based reinforcement learning without policies 2. Today: model-based reinforcement learning of policies • Learning global policies • Learning local polic

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PowerPoint Presentation Reframing Control as an Inference Problem CS 285: Deep Reinforcement Learning, Decision Making, and Control Sergey Levine Class Notes 1. Homework 3 is out! Due Oct 21 • Start early, this one will take a bit longer! Today’s Lecture 1. Does reinforcement learning and optimal control provide a reasonable model of human behavior? 2. Is there a better explanation? 3. Can we deri

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PowerPoint Presentation Inverse Reinforcement Learning CS 285: Deep Reinforcement Learning, Decision Making, and Control Sergey Levine Today’s Lecture 1. So far: manually design reward function to define a task 2. What if we want to learn the reward function from observing an expert, and then use reinforcement learning? 3. Apply approximate optimality model from last week, but now learn the reward

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Study Circle in Deep Reinforcement Learning Lecture 0 Gautham Nayak Seetanadi Dept. of Automatic Control, Lund Institute of Technology February 9, 2021 Study Circle I We will follow online courses and assignments I The topics might change over time I Happy for input or suggestions for the course I Current course ends Mid-April. Might speed up at the end I Active participation in course for credits

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Deep RL Assignment 1: Imitation Learning Fall 2019 due September 16th, 11:59 pm The goal of this assignment is to experiment with imitation learning, including direct behavior cloning and the DAgger algorithm. In lieu of a human demonstrator, demonstrations will be provided via an expert policy that we have trained for you. Your goals will be to set up behavior cloning and DAgger, and compare thei

https://www.control.lth.se/fileadmin/control/Education/DoctorateProgram/StudyCircleDeepReinforcementLearning/cs285_hw1.pdf - 2025-06-20

CS285 Deep Reinforcement Learning HW3: Q-Learning and Actor-Critic Due: October 21st 2019, 11:59 pm 1 Part 1: Q-Learning 1.1 Introduction Part 1 of this assignment requires you to implement and evaluate Q-learning with convolutional neural networks for playing Atari games. The Q-learning algorithm was covered in lecture, and you will be provided with starter code. A GPU machine will be faster, but

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Study Circle in Reinforcement Learning Study Circle in Reinforcement Learning Coordinator: Karl-Erik Årzén Study Circle • A study circle and not a course • I know probably much less about RL than you do • Active participation Lectures and Meetings • The University College London (UCL) course ”Reinforcement Learning” by David Silver • 10 Video Lectures • Accompanying slides • Exercises • Code • Mee

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repetition.dvi Matematikrepetition inför Reglerteknik AK Maria Karlsson 2005 I kursen Reglerteknik AK används en hel del matematik från gymnasiet och kurserna i Analys och Linjär algebra. Från tidigare år vet vi att en del studenter tycker att kursen i reglerteknik är svår i början eftersom man inte repeterat denna matematik på ett par år. Här visar vi exempel på beräkningar du kommer att behöva g

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FRTF05 Automatic Control Basic Course for F, I, Pi + TFRG95 Course Program Spring 2024 Updated Jan 9, 2024 1 Lectures The course consists of 15 lectures (30 hours). All lectures are held in M:A (Ole Römers väg 1). Lectures are held in Swedish. We will mainly use the whiteboard, but any additional materials such as compendia, exercises and lab manuals are available on Canvas. Schedule: See the we

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19 August 2014 English version - November 2014 Instructions for a written critical review on degree projects at LTH The critical review (a peer review) is to be both oral and written and both parts are to be assessed by the examiner of the student defending the project (the respondent). The reviewer is responsible for providing a written review of the degree project to both the respondent and the

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Microsoft Word - Teknisk rapportskrivning.doc In d u st ri a l E le c tr ic a l E n g in e e ri n g a n d A u to m a ti o n Teknisk rapportskrivning Gertrud Pettersson (Nordiska Språk) Gustaf Olsson (IEA) Mats Alaküla (IEA) Dept. of Industrial Electrical Engineering and Automation Lund University Innehåll Förord 2 1 Inledning 3 1.1 Syftet med denna skrift 3 1.2 Olika slag av skrivande 3 1.3 Hur ås

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forskarutb.dvi FORSKARUTBILDNING I REGLERTEKNIK Varför forskarutbildning? Gillar Du kreativt arbete, där dina kunskaper och färdigheter får prövas på nya problemställningar? Vill Du kombinera frihet med ansvar? Uppskattar Du internationella kontakter och utlandsvistelser? Då är forskarutbildning något för Dig. Forskarutbildningens mål är att utveckla dokto- randens färdigheter i ett visst ämnesomr

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FRT041ident.dvi REGLERTEKNIK LTH KURSINFORMATION SYSTEMIDENTIFIERING FRT041 Vad är systemidentifiering? Identifiering och modellbygge behövs i tidiga faser av tekniskt och vetenskapligt arbete för att bestäm- ma empiriska beroenden mellan uppmätta variab- ler. Även om reglertekniska modeller har en cen- tral plats, innefattar därför tillämpningsområdena även experimentella och dataorienterade vete

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