PID Control Karl Johan Åström Tore Hägglund Department of Automatic Control, Lund University September 23, 2020 PID Control 1. Introduction 2. The Controller 3. Stability 4. Performance and Robustness 5. Empirical Tuning Rules 6. Tuning based on Optimization 7. Relay Auto-tuning 8. Limitations of PID Control 9. Summary Theme: The most common controller. Introduction ◮ PID control is widely used in

https://www.control.lth.se/fileadmin/control/Education/DoctorateProgram/ControlSystemsSynthesis/2020/PIDeight.pdf - 2025-02-07

Control System Synthesis - PhD Class Exercise session 2 October 8, 2020 1 Inverted pendulum on a cart Figure 1: Inverted pendulum. The equations of motion are : (M +m)ẍ+ bẋ+mlθ̈ cos θ −mlθ̇2 sin θ = F (J +ml2)θ̈ +mgl sin θ = −mlẍ cos θ (1) where: • M = 0.5kg is the mass of the cart • m = 0.2kg is the mass of the pendulum • b = 0.1N/m/sec is the coefficient of friction for the cart • l = 0.3m is

https://www.control.lth.se/fileadmin/control/Education/DoctorateProgram/ControlSystemsSynthesis/2020/PhD_Class___exercise_session_2.pdf - 2025-02-07

Control System Synthesis - PhD Class Handin 1: Temperature control in a heat exchanger 24/09/2020 A chemical reactor called “stirring tank” is depicted below. The top inlet delivers liquid to be mixed in the tank. The tank liquid must be maintained at a constant temperature by varying the amount of steam supplied to the heat exchanger (bottom pipe) via its control valve. Variations in the temperat

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flexservo.dvi Handin - Flexible Servo The process consists of three horizontal pulleys connected by two elastic belts. SensorDC motor The transfer function from motor to sensor can take 3 forms (Ts = 50ms): Unloaded: B = 0.28261z−3 + 0.50666z−4 A = 1− 1.41833z−1 + 1.58939z−2 − 1.31608z−3 + 0.88642z−4 Half Load: B = 0.10276z−3 + 0.18123z−4 A = 1− 1.99185z−1 + 2.20265z−2 − 1.84083z−3 + 0.89413z−4 Fu

https://www.control.lth.se/fileadmin/control/Education/DoctorateProgram/ControlSystemsSynthesis/2020/handin2.pdf - 2025-02-07

Deep Learning for Nature Language Processing Deep Learning for Nature Language Processing Lianhao Yin Lund University lianhao.yin@energy.lth.se November 29, 2016 Lianhao Yin (LTH) DL for NLP November 29, 2016 1 / 21 Overview 1 Introduction 2 Word2vec 3 Recurrent Neural Networks 4 Dynamic Memory Networks Lianhao Yin (LTH) DL for NLP November 29, 2016 2 / 21 Natural Language Processing Natural langu

https://www.control.lth.se/fileadmin/control/Education/DoctorateProgram/DeepLearning/2016/NLPLianhao.pdf - 2025-02-07

Deep-Learning Study Circle: Reinforcement Learning Deep-Learning Study Circle: Reinforcement Learning Gabriel Ingesson 0/46 Reinforcement Learning The problem where an agent has to learn a policy (behavior) by taking actions in an environment, with the goal that the policy should maximize a cumulative reward. Different from supervised and unsupervised learning: No labeled training data. Reward sig

https://www.control.lth.se/fileadmin/control/Education/DoctorateProgram/DeepLearning/2016/RL.pdf - 2025-02-07

Autoencoders Autoencoders Fredrik Bagge Carlson Fredrik Bagge Carlson, Lund University: Autoencoders Introduction General idea Auto: Greek auto- "self, one’s own" Encode: from en- "make, put in" + code: a system of words, letters, figures, or symbols used to represent others Find a useful encoding, h = f(x), of data x in an unsupervised manner. Trained using an encoder h = f(x) and a decoder x̂ =

https://www.control.lth.se/fileadmin/control/Education/DoctorateProgram/DeepLearning/2016/autoencoders.pdf - 2025-02-07

Practical overview of optimization of Deep Networks Practical overview of optimization of Deep Networks Carl Åkerlindh December 15, 2016 Carl Åkerlindh | DL Training 2 / 19 Gradient descent optimization Backpropagation Batch gradient descent Online gradient descent Mini-batch gradient descent Challenges Gradient descent additions Momentum Nestrov accelerated gradient Adagrad Other SGD variants Add

https://www.control.lth.se/fileadmin/control/Education/DoctorateProgram/DeepLearning/2016/dl_optimization.pdf - 2025-02-07

Fast Learning of Assembly Tasks using Dynamic Movement Primitives and Deterministic Policy Gradients Fast Learning of Assembly Tasks using Dynamic Movement Primitives and Deterministic Policy Gradients Fredrik Bagge Carlson* Martin Karlsson 1 / 10 Introduction One-shot learning using DMP Update DMP using reinforcement learning Learn sensor-feedback controller with DMP as nominal controller Introdu

https://www.control.lth.se/fileadmin/control/Education/DoctorateProgram/DeepLearning/2016/dmp_dpg_pres.pdf - 2025-02-07

Improving Imputation Using Stacked denoising Autoencoder Improving Imputation Using Stacked denoising Autoencoder Najmeh Abiri November 22, 2016 Computational Biology and Biological Physics Missing Data Pre-processing data Astronomy Outlier? Biology Missing Data? 1 Missing data in Biology Molecular Patterns of Life 2 Missing data in Biology Generate detailed DNA/protein molecular fingerprints and

https://www.control.lth.se/fileadmin/control/Education/DoctorateProgram/DeepLearning/2016/improving-imputation-stacked.pdf - 2025-02-07

Deep Learning on GPU Mattias Fält Dept. of Automatic Control Lund Institute of Technology Mattias Fält Deep Learning on GPU Overview What is the difference between CPU and GPU? What is CUDA, and how does it relate to cuBLAS and cuDNN? How is this connected to Deep Learning and Tensorflow? How do I run tensorflow on the GPU? What is a TPU? Mattias Fält Deep Learning on GPU GPU vs CPU http://allegro

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https://www.control.lth.se/fileadmin/control/Education/DoctorateProgram/HistoryOfControl/2016/AndronovOnVyshnegradski.pdf - 2025-02-07

Untitled 1 History of Cont rol - Int roduc tion Karl Johan Åström Department of Automatic Control LTH Lund University Int roduc tion 1. Introduction 2. Practical Information 3. A Thumbnail History 4. The Power of Feedback 5. Summary Theme: Those who ignore history are doomed to repeat it. Those who cannot remember the past are condemned to repeat it. (George Santayana) Why bot her? ◮ Fun ◮ Useful

https://www.control.lth.se/fileadmin/control/Education/DoctorateProgram/HistoryOfControl/2016/L01introductioneight.pdf - 2025-02-07

Untitled 1 Process Cont rol Karl Johan Åström Department of Automatic Control LTH Lund University Process Cont rol K. J. Åström 1. Introduction 2. The Industrial Scene 3. Pneumatics 4. Theory? 5. Tuning 6. More Recent Development 7. Summary Theme: Measurement Control Instrumentation and Communication (pneumatic). Lectures 1940 1960 2000 1 Introduction 2 Governors | | | 3 Process Control | | | 4 Ae

https://www.control.lth.se/fileadmin/control/Education/DoctorateProgram/HistoryOfControl/2016/L03ProcessControleight.pdf - 2025-02-07

L05ASMENyquistLecture.pdf A S M E N y q u is t L e c tu re 2 0 0 5 N yq u is t an d H is S em in al P ap er s K ar l J o h an Å st rö m D ep ar tm en t o f M ec h an ic al E n g in ee ri n g U n iv er si ty o f C al if o rn ia S an ta B ar b ar a A S M E N y q u is t L e c tu re 2 0 0 5 H ar ry N yq u is t 18 89 -1 97 6 A G if te d S ci en ti st a n d E n g in ee r Jo h n so n -N yq u is t n o is

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() Automatic Control Emerges Karl Johan Åström Department of Automatic Control LTH Lund University Karl Johan Åström Automatic Control Emerges Automatic Control Emerges K. J. Åström 1 Introduction 2 The Computing Bottleneck 3 State of the Art around 1940 4 WWII 5 Servomechanisms 6 Summary Theme: Unification, theory, and analog computing. Karl Johan Åström Automatic Control Emerges Lectures 1940 19

https://www.control.lth.se/fileadmin/control/Education/DoctorateProgram/HistoryOfControl/2016/L07ControlEmerges.pdf - 2025-02-07

Untitled 1 Automatic Cont rol in Sweden Karl Johan Åström Department of Automatic Control, LTH Lund University Lectures 1940 1960 2000 1 Introduction 2 Governors | | | 3 Process Control | | | 4 Feedback Amplifiers | | | 5 Harry Nyquist | | | 6 Aerospace | | | 7 Automatic Control Emerges ← | | 8 The Second Phase ← ← | 9 Automatic Control in Sweden | | | 10 Automatic Control in Lund | | 11 The Futur

https://www.control.lth.se/fileadmin/control/Education/DoctorateProgram/HistoryOfControl/2016/L09Swedeneight.pdf - 2025-02-07

L11Future.pdf Some personal reflections The Future of Control K. J. Åström Department of Automatic Control LTH Lund University LTH April 24 2012 NAE, AFOSR, IEEE, IFAC LTH April 24 2012 The Systems Perspective In the past steady increases in knowledge has spawned new microdisciplines within engineering. However, contemporary challenges – from biomedical devices to complex manufacturing designs to

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governors.dvi ON GOVERNORS J.C. MAXWELL From the Proceedings of the Royal Society, No.100, 1868. A GOVERNOR is a part of a machine by means of which the velocity of the machine is kept nearly uniform, notwithstanding variations in the driving-power or the resistance. Most governors depend on the centrifugal force of a piece connected with a shaft of the machine. When the velocity increases, this f

https://www.control.lth.se/fileadmin/control/Education/DoctorateProgram/HistoryOfControl/2016/MaxwellOnGovernors.pdf - 2025-02-07