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Literature (Motion Planning Course 2019) | Department of Automatic Control Faculty of Engineering, LTH Search Department of Automatic Control LTH, Faculty of Engineering Education Research External Engagement Personnel Publications About, Contact Home > Education > Doctorate Program > Motion Planning and Control > Literature (Motion Planning Course 2019) Denna sida på svenska This page in
Eveline Gottzein | Department of Automatic Control Faculty of Engineering, LTH Search Department of Automatic Control LTH, Faculty of Engineering Education Research External Engagement Personnel Publications About, Contact Home > External Engagement > Female Influencers in Automatic Control > Historical Female Influencers in Automatic Control > Eveline Gottzein Denna sida på svenska This p
Francoise Lamnabhi-Lagarrigue | Department of Automatic Control Faculty of Engineering, LTH Search Department of Automatic Control LTH, Faculty of Engineering Education Research External Engagement Personnel Publications About, Contact Home > External Engagement > Female Influencers in Automatic Control > Historical Female Influencers in Automatic Control > Francoise Lamnabhi-Lagarrigue De
Untitled 1 Automatic Cont rol in Lund Karl Johan Åström Department of Automatic Control, LTH Lund University Automatic Cont rol in Lund 1. Introduction 2. System Identification and Adaptive Control 3. Computer Aided Control Engineering 4. Relay Auto-tuning 5. Two Applications 6. Summary Theme: Building a New Department and Samples of Activities. Lectures 1940 1960 2000 1 Introduction 2 Governors |
A Brief History of Event-Based Control Marcus T. Andrén Department of Automatic Control Lund University Marcus T. Andrén A Brief History of Event-Based Control Concept of Event-Based Example with impulse control [Åström & Bernhardsson, 1999] Periodic Sampling Event-Based Sampling Event-Based: Trigger sampling and actuation based on signal property, e.g |x(t )| >δ (Lebesgue sampling) A.k.a aperiodi
A Course in Optimal Control and Optimal Transport Dongjun Wu dongjun.wu@control.lth.se August, 2023 i CONTENTS Contents 1 1 Dynamic Programming 5 1.1 Discrete time systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.1.1 Shortest path problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.1.2 Optimal control on finite horizon .
L1-Introduction 2022-03-07 1 Modeling Karl Johan Åström Department of Automatic Control LTH Lund University from Physics to Languages and Software 1 Modeling ØEssential for the development of science, example: Brahe, Kepler, Newton Ø Essential element of all engineering Ø Process design and optimization Ø Insight and understanding Ø Control design and optimization Ø Implementation – The internal m
PowerPoint Presentation Equation and Object-oriented Modeling Modeling Course – Automatic Control Hilding Elmqvist Mogram AB and Modelon AB In collaboration with: Martin Otter, Gerhard Hippman, Andrea Neumayr, Oskar Åström Assistants: Karl Johan Åström and Oskar Åström Content • Introduction • Part 1: Equation Oriented Modeling (Modia) • structural and symbolic algorithms • DAE index reduction • e
Automotive Modeling—An Overview of Model Components Contents: 1. Introduction 2. Propulsion and powertrain dynamics 3. Braking system and wheel dynamics 4. Tire–road interaction models 5. Steering and suspension dynamics 6. Chassis dynamics 7. Experiments and model calibration 8. Summary Lecture on May 5: Mathias Strandberg from Modelon will discuss automotive modeling using Modelica and Modelon I
https://www.control.lth.se/fileadmin/control/Education/DoctorateProgram/PhysicalModeling/Lectures/L9B-Automotive.pdf - 2025-10-12
Physical modeling – Power systems Physical modelling – AC Power systems OLOF SAMUELSSON, INDUSTRIAL ELECTRICAL ENGINEERING AND AUTOMATIO N E S A V MW and Mvar Outline • The electric power system • Electromagnetic transients • Phasor model at steady state – power flow • Electro-mechanical and mechanical oscillations • Dynamic phasor simulation • Linearized DAE and ODE • Modal analysis • Case study:
Neurons and Neuroscience 1. Introduction 2. Neurobiology 3. Simple models of a single neuron 4. Systems with a few neurons 5. Silicon neurons 6. Event based control 7. Summary Introduction ◮ A major challenge ◮ Golgi staining 1885 ◮ Cajal 1911 Mapping of the neurons using Golgi staining ◮ McCulloch and Pitts 1943 ◮ Wiener 1948 Cybernetics - Control and Communication in the Animal and the Machine ◮
https://www.control.lth.se/fileadmin/control/Education/DoctorateProgram/PhysicalModeling/Lectures/neuronseight.pdf - 2025-10-12
PowerPoint Presentation Optimal Control and Planning CS 285: Deep Reinforcement Learning, Decision Making, and Control Sergey Levine Class Notes 1. Homework 3 is out! • Start early, this one will take a bit longer! Today’s Lecture 1. Introduction to model-based reinforcement learning 2. What if we know the dynamics? How can we make decisions? 3. Stochastic optimization methods 4. Monte Carlo tree
PowerPoint Presentation Model-Based Reinforcement 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. Basics of model-based RL: learn a model, use model for control • Why does naïve approach not work? • The effect of distributional shift in model-based RL 2. Uncer
PowerPoint Presentation Deep RL with Q-Functions CS 285: Deep Reinforcement Learning, Decision Making, and Control Sergey Levine Class Notes 1. Homework 2 is due next Monday 2. Project proposal due 9/25, that’s today! • Remember to upload to both Gradescope and CMT (see Piazza post) Today’s Lecture 1. How we can make Q-learning work with deep networks 2. A generalized view of Q-learning algorithms
PowerPoint Presentation Advanced Policy Gradients CS 285: Deep Reinforcement Learning, Decision Making, and Control Sergey Levine Class Notes 1. Homework 2 due today (11:59 pm)! • Don’t be late! 2. Homework 3 comes out this week • Start early! Q-learning takes a while to run Today’s Lecture 1. Why does policy gradient work? 2. Policy gradient is a type of policy iteration 3. Policy gradient as a c
CS285 Deep Reinforcement Learning HW4: Model-Based RL Due November 4th, 11:59 pm 1 Introduction The goal of this assignment is to get experience with model-based reinforcement learning. In general, model-based reinforcement learning consists of two main parts: learning a dynamics function to model observed state transitions, and then using predictions from that model in some way to decide what to
https://www.control.lth.se/fileadmin/control/Education/DoctorateProgram/StudyCircleDeepReinforcementLearning/hw4.pdf - 2025-10-12
Microsoft Word - Anvisningar för opposition 19 augusti 14 19 augusti 2014 Anvisningar för opposition på examensarbeten vid LTH Oppositionen ska vara både muntlig och skriftlig och båda delar ska godkännas av respondentens examinator. Opponenten ansvarar för att respondenten och dennes examinator, senast vid seminariet där examensarbetet presenteras, får en skriftlig version av oppositionen (opposi
Lecturenotes.dvi A ISRN KTH/OPT SYST/FR 00/12 SECoden: TRITA/MAT-00-OS12ISSN 1401-2294 DEPARTMENT OF MATHEMATICSROYAL INSTITUTE OF TECHNOLOGYSE-100 44 STOCKHOLM, SWEDEN ISSN 1401-2294 Le ture Notes on Integral Quadrati ConstraintsbyUlf JonssonOptimization and Systems Theory Le tures on Input-Output Stability and IntegralQuadrati ConstraintsUlf JonssonDivision of Optimization and Systems TheoryRoya
https://www.control.lth.se/fileadmin/control/staff/AndersRantzer/ULF_J_IQC_Lecturenotes.pdf - 2025-10-12
Errata ULF_J_IQC_Lecturenotes.ps (Errata från Michael Muhler:) Page Line Typo Correction 2 10 S.t Petersburg St. Peterburg 2 12 into unified into a unified 2 16 have contributed has contributed 6 -9 realizations on the realizations of the 6 -8 systems defines operators systems define operators 7 17 is a exact is an exact 9 1 the input u_1 and u_2 the inputs u_1 and u_2 9 9 causality need to causal
https://www.control.lth.se/fileadmin/control/staff/AndersRantzer/errata_ULF_J_IQC_Lecturenotes.txt - 2025-10-12
05 - Hello k8s! Slide title 70 pt CAPITALS Slide subtitle Cloud Native #5 - Hello K8s! Ericsson Internal | 2018-02-21 git clone http://github.com/kubernetes-up-and-running/examples Hands-on with Kubernetes This Session http://github.com/kubernetes-up-and-running/examples http://github.com/kubernetes-up-and-running/examples Ericsson Internal | 2018-02-21 Containers at scale Containers is great tech
https://www.control.lth.se/fileadmin/control/staff/JohanEker/05_-_Hello_k8s_.pdf - 2025-10-12
