The .LBR file format was an archive file format used on CP/M and DOS operating systems during the early 1980s. .LBR files were created by the LU program; later programs like NULU arrived for .LBR creation, and many tools such as LT and QL were capable of extracting from .LBR archives. .LBR is an abbreviation of "Library", and, resembling the .tar file format, member files were only stored in the .LBR file, not compressed. As transfer of LBR files by modem was common, it was typical practice for archiving a collection of files to compress them using the SQ or CRUNCH programs then store them in an .LBR archive, or else (more rarely) store the files in the LBR archive, then use SQ or CRUNCH to compress the archive. A compressed LBR archive file was given the extension ".LQR" (if squeezed) or ".LZR" (if crunched); however, it was more common to compress the members of the archive than to compress the archive as a whole.
As MS-DOS and other operating systems became more popular and displaced CP/M, .LBR's popularity waned. The development of the ARC archiver which both compressed and archived files in one program went a long way towards displacing .LBR on MS-DOS systems; on CP/M systems, .LBR persisted longer due to the lack of a useful ARC port.
What Is Linear Quadratic Regulator (LQR) Optimal Control? | State Space, Part 4
Check out the other videos in the series: https://youtube.com/playlist?list=PLn8PRpmsu08podBgFw66-IavqU2SqPg_w
Part 1 - The state space equations: https://youtu.be/hpeKrMG-WP0
Part 2 - Pole placement: https://youtu.be/FXSpHy8LvmY
Part 3 - Observability and Controllability: https://youtu.be/BYvTEfNAi38
The Linear Quadratic Regulator (LQR)
LQR is a type of optimal control that is based on state space representation. In this video, we introduce this topic at a very high level so that you walk away with a general understanding of the control problem and can build on this understanding when you are studying the math behind it. This video will cover what it means to be optimal and how to think about the LQR problem. At the end I’ll show you some examples in MATLAB that I think will help you ga...
published: 05 Feb 2019
Why the Riccati Equation Is important for LQR Control
This Tech Talk looks at an optimal controller called linear quadratic regulator, or LQR, and shows why the Riccati equation plays such an important role in solving it efficiently.
The talk walks through three different ways that the LQR problem can be solved: an intuitive, but ultimately inefficient brute force way; a more efficient learning algorithm way; and then the most efficient approach, which is accomplished analytically using the algebraic Riccati equation.
Want to see all the references in a nice, organized list? Check out this journey on Resourcium: https://bit.ly/3NOIWeg
- Design and Simulate Kalman Filter Algorithms: https://bit.ly/3Obq3n5
- Explanation of “Completing the Square” for LQR by Laurent Lessard: https://bit.ly/3Dio9e5
- Train Custom LQR Agent: https://bit.ly/450R...
published: 02 Aug 2023
Introduction to Linear Quadratic Regulator (LQR) Control
In this video we introduce the linear quadratic regulator (LQR) controller. We show that an LQR controller is a full state feedback controller where the gain matrix K is computed by solving an optimization problem.
Topics and time stamps:
0:00 - Introduction
4:01 - Introduction to Optimization
19:24 - Setting up the cost function (Q and R matrices)
41:36 - Solving the Algebraic Ricatti Equation
1:01:05 - Example of LQR in Matlab
1:17:09 - Using LQR to address practical implementation issues with full state feedback controllers
In addition, some videos that may be helpful or relevant to the discussion at hand include:
-Introduction to Full State Feedback Control (https://youtu.be/1zIIcYfp5QA)
-Practical Implementation Issues with a Full State Feedback Controller (https://youtu.be/9vCT...
published: 03 Dec 2018
LQR controller for tracking rather than just regulating! An example in Matlab
This video shows how to use LQR controller to enforce a state in a given dynamic system (state space) to track a desired reference rather than be regulated to zero.
Also watch:
LQG controller for tracking a given reference! An example in Matlab: https://youtu.be/7v7U-ydCWV8
Type 1 Servo (reference tracking) for Plants with no Integrator – Inverted pendulum with MATLAB Code: https://youtu.be/PlWwDDyQ7C0
published: 06 Oct 2022
PID controller Vs LQR Controller for rotary inverted pendulum || STRIPS 1.0
published: 12 Dec 2021
Linear Quadratic Regulator (LQR) Control for the Inverted Pendulum on a Cart [Control Bootcamp]
Here we design an optimal full-state feedback controller for the inverted pendulum on a cart example using the linear quadratic regulator (LQR). In Matlab, we find that this is a simple one-line command 'lqr'.
These lectures follow Chapter 8 from:
"Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Brunton and Kutz
Amazon: https://www.amazon.com/Data-Driven-Science-Engineering-Learning-Dynamical/dp/1108422098
Chapters available at: http://databookuw.com/databook.pdf
Book Website: http://databookuw.com
Brunton Website: eigensteve.com
This video was produced at the University of Washington
published: 29 Jan 2017
Self Balance Robot Ardra Lab | PID vs LQR | position locking | freelancing
The robot is able to maneuver in any terrain with balancing it self on 2 wheels
We #design #products with #research and #excellence so that you can #commit to your #customers.
- Electronics prototyping
- Embedded systems design
- Robotics and automation
- Internet of Things (IoT) solutions
- Circuit and PCB design
- Firmware development
Visit: ardralab.com
published: 12 Nov 2022
Linear Quadratic Regulator (LQR) in Python - Detailed Explanation - Control Engineering Tutorial
#controltheory #robotics #controlengineering #mechatronics #machinelearning #electricalengineering #signalprocessing #python #pythontutorial #signals_systems #machinelearning #optimization #robotics #electricalengineering #mechanicalengineering #autonomoussystems #robots #simulation #optimalcontrol
It takes a significant amount of time and energy to create these free video tutorials. You can support my efforts in this way:
- Buy me a Coffee: https://www.buymeacoffee.com/AleksandarHaber
- PayPal: https://www.paypal.me/AleksandarHaber
- Patreon: https://www.patreon.com/user?u=32080176&fan_landing=true
- You Can also press the Thanks YouTube Dollar button
In this control systems and control engineering tutorial, we explain how to implement the linear quadratic regulator (LQR) in Python. We ...
published: 01 Aug 2023
Core Concepts: Linear Quadratic Regulators
We explore the concept of control in robotics, notably Linear Quadratic Regulators (LQR). We see that a powerful way to think about control is as a dynamic optimization, where the goal is to compute a mapping from states to action that minimize a user-specified cost, e.g. land a rocket without exploding. Moreover, you can do this efficiently thanks to Bellman’s insight that the optimal value of a state at your current time is simply the minimum one-step cost + the optimal value at the next time, suggesting an elegant iterative procedure. What makes LQR special is that you can do this analytically because the optimal value function is a quadratic! This property grants LQR a first-class status as theoretically fundamental and practically powerful.
Notebook for LQR applied to inverted pendu...
published: 25 Nov 2021
Overview of LQR for System Control
This video describes the core component of optimal control, developing the optimization algorithm for solving for the optimal control values assuming one has access to all state variables.
Check out the other videos in the series: https://youtube.com/playlist?list=PLn8PRpmsu08podBgFw66-IavqU2SqPg_w
Part 1 - The state space equations: https://yout...
This Tech Talk looks at an optimal controller called linear quadratic regulator, or LQR, and shows why the Riccati equation plays such an important role in solv...
In this video we introduce the linear quadratic regulator (LQR) controller. We show that an LQR controller is a full state feedback controller where the gain m...
In this video we introduce the linear quadratic regulator (LQR) controller. We show that an LQR controller is a full state feedback controller where the gain matrix K is computed by solving an optimization problem.
Topics and time stamps:
0:00 - Introduction
4:01 - Introduction to Optimization
19:24 - Setting up the cost function (Q and R matrices)
41:36 - Solving the Algebraic Ricatti Equation
1:01:05 - Example of LQR in Matlab
1:17:09 - Using LQR to address practical implementation issues with full state feedback controllers
In addition, some videos that may be helpful or relevant to the discussion at hand include:
-Introduction to Full State Feedback Control (https://youtu.be/1zIIcYfp5QA)
-Practical Implementation Issues with a Full State Feedback Controller (https://youtu.be/9vCTokJ5RQ8)
-Introduction to Linear State Estimation (TBD)
The Matlab/Simulink files associated with this video are located at:
-http://faculty.washington.edu/lum/EducationalVideoFiles/Controls05/LQRIntro.nb
-http://faculty.washington.edu/lum/EducationalVideoFiles/Controls05/IntroToLQR.m
-http://faculty.washington.edu/lum/EducationalVideoFiles/Controls05/IntroToLQR_model.slx
You can support this channel via Patreon at https://www.patreon.com/christopherwlum. Thank you for your help!
In this video we introduce the linear quadratic regulator (LQR) controller. We show that an LQR controller is a full state feedback controller where the gain matrix K is computed by solving an optimization problem.
Topics and time stamps:
0:00 - Introduction
4:01 - Introduction to Optimization
19:24 - Setting up the cost function (Q and R matrices)
41:36 - Solving the Algebraic Ricatti Equation
1:01:05 - Example of LQR in Matlab
1:17:09 - Using LQR to address practical implementation issues with full state feedback controllers
In addition, some videos that may be helpful or relevant to the discussion at hand include:
-Introduction to Full State Feedback Control (https://youtu.be/1zIIcYfp5QA)
-Practical Implementation Issues with a Full State Feedback Controller (https://youtu.be/9vCTokJ5RQ8)
-Introduction to Linear State Estimation (TBD)
The Matlab/Simulink files associated with this video are located at:
-http://faculty.washington.edu/lum/EducationalVideoFiles/Controls05/LQRIntro.nb
-http://faculty.washington.edu/lum/EducationalVideoFiles/Controls05/IntroToLQR.m
-http://faculty.washington.edu/lum/EducationalVideoFiles/Controls05/IntroToLQR_model.slx
You can support this channel via Patreon at https://www.patreon.com/christopherwlum. Thank you for your help!
This video shows how to use LQR controller to enforce a state in a given dynamic system (state space) to track a desired reference rather than be regulated to z...
This video shows how to use LQR controller to enforce a state in a given dynamic system (state space) to track a desired reference rather than be regulated to zero.
Also watch:
LQG controller for tracking a given reference! An example in Matlab: https://youtu.be/7v7U-ydCWV8
Type 1 Servo (reference tracking) for Plants with no Integrator – Inverted pendulum with MATLAB Code: https://youtu.be/PlWwDDyQ7C0
This video shows how to use LQR controller to enforce a state in a given dynamic system (state space) to track a desired reference rather than be regulated to zero.
Also watch:
LQG controller for tracking a given reference! An example in Matlab: https://youtu.be/7v7U-ydCWV8
Type 1 Servo (reference tracking) for Plants with no Integrator – Inverted pendulum with MATLAB Code: https://youtu.be/PlWwDDyQ7C0
Here we design an optimal full-state feedback controller for the inverted pendulum on a cart example using the linear quadratic regulator (LQR). In Matlab, we ...
Here we design an optimal full-state feedback controller for the inverted pendulum on a cart example using the linear quadratic regulator (LQR). In Matlab, we find that this is a simple one-line command 'lqr'.
These lectures follow Chapter 8 from:
"Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Brunton and Kutz
Amazon: https://www.amazon.com/Data-Driven-Science-Engineering-Learning-Dynamical/dp/1108422098
Chapters available at: http://databookuw.com/databook.pdf
Book Website: http://databookuw.com
Brunton Website: eigensteve.com
This video was produced at the University of Washington
Here we design an optimal full-state feedback controller for the inverted pendulum on a cart example using the linear quadratic regulator (LQR). In Matlab, we find that this is a simple one-line command 'lqr'.
These lectures follow Chapter 8 from:
"Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Brunton and Kutz
Amazon: https://www.amazon.com/Data-Driven-Science-Engineering-Learning-Dynamical/dp/1108422098
Chapters available at: http://databookuw.com/databook.pdf
Book Website: http://databookuw.com
Brunton Website: eigensteve.com
This video was produced at the University of Washington
The robot is able to maneuver in any terrain with balancing it self on 2 wheels
We #design #products with #research and #excellence so that you can #commit to y...
The robot is able to maneuver in any terrain with balancing it self on 2 wheels
We #design #products with #research and #excellence so that you can #commit to your #customers.
- Electronics prototyping
- Embedded systems design
- Robotics and automation
- Internet of Things (IoT) solutions
- Circuit and PCB design
- Firmware development
Visit: ardralab.com
The robot is able to maneuver in any terrain with balancing it self on 2 wheels
We #design #products with #research and #excellence so that you can #commit to your #customers.
- Electronics prototyping
- Embedded systems design
- Robotics and automation
- Internet of Things (IoT) solutions
- Circuit and PCB design
- Firmware development
Visit: ardralab.com
#controltheory #robotics #controlengineering #mechatronics #machinelearning #electricalengineering #signalprocessing #python #pythontutorial #signals_systems #machinelearning #optimization #robotics #electricalengineering #mechanicalengineering #autonomoussystems #robots #simulation #optimalcontrol
It takes a significant amount of time and energy to create these free video tutorials. You can support my efforts in this way:
- Buy me a Coffee: https://www.buymeacoffee.com/AleksandarHaber
- PayPal: https://www.paypal.me/AleksandarHaber
- Patreon: https://www.patreon.com/user?u=32080176&fan_landing=true
- You Can also press the Thanks YouTube Dollar button
In this control systems and control engineering tutorial, we explain how to implement the linear quadratic regulator (LQR) in Python. We use a mass spring damper system as a test case. We design an LQR controller for non-zero set points. This means that we need to design a steady-state desired control inputs. We use the Python Control Systems Library to implement the LQR controller.
#controltheory #robotics #controlengineering #mechatronics #machinelearning #electricalengineering #signalprocessing #python #pythontutorial #signals_systems #machinelearning #optimization #robotics #electricalengineering #mechanicalengineering #autonomoussystems #robots #simulation #optimalcontrol
It takes a significant amount of time and energy to create these free video tutorials. You can support my efforts in this way:
- Buy me a Coffee: https://www.buymeacoffee.com/AleksandarHaber
- PayPal: https://www.paypal.me/AleksandarHaber
- Patreon: https://www.patreon.com/user?u=32080176&fan_landing=true
- You Can also press the Thanks YouTube Dollar button
In this control systems and control engineering tutorial, we explain how to implement the linear quadratic regulator (LQR) in Python. We use a mass spring damper system as a test case. We design an LQR controller for non-zero set points. This means that we need to design a steady-state desired control inputs. We use the Python Control Systems Library to implement the LQR controller.
We explore the concept of control in robotics, notably Linear Quadratic Regulators (LQR). We see that a powerful way to think about control is as a dynamic opti...
We explore the concept of control in robotics, notably Linear Quadratic Regulators (LQR). We see that a powerful way to think about control is as a dynamic optimization, where the goal is to compute a mapping from states to action that minimize a user-specified cost, e.g. land a rocket without exploding. Moreover, you can do this efficiently thanks to Bellman’s insight that the optimal value of a state at your current time is simply the minimum one-step cost + the optimal value at the next time, suggesting an elegant iterative procedure. What makes LQR special is that you can do this analytically because the optimal value function is a quadratic! This property grants LQR a first-class status as theoretically fundamental and practically powerful.
Notebook for LQR applied to inverted pendulum: https://colab.research.google.com/drive/1ofAmq3M4lMMXfSOpR-B_38WPSxDqSDdM?usp=sharing
References:
1. Bagnell, Boots: https://homes.cs.washington.edu/~bboots/RL-Fall2020/Lectures/LQR_notes.pdf
2. Russ Tedrake's lecture: https://underactuated.mit.edu/lqr.html
Check out the full series "Core Concept in Robotics": https://www.youtube.com/playlist?list=PLQZQ7N26C6bbhXHuongGbbeVLl-PKkJ6K
We explore the concept of control in robotics, notably Linear Quadratic Regulators (LQR). We see that a powerful way to think about control is as a dynamic optimization, where the goal is to compute a mapping from states to action that minimize a user-specified cost, e.g. land a rocket without exploding. Moreover, you can do this efficiently thanks to Bellman’s insight that the optimal value of a state at your current time is simply the minimum one-step cost + the optimal value at the next time, suggesting an elegant iterative procedure. What makes LQR special is that you can do this analytically because the optimal value function is a quadratic! This property grants LQR a first-class status as theoretically fundamental and practically powerful.
Notebook for LQR applied to inverted pendulum: https://colab.research.google.com/drive/1ofAmq3M4lMMXfSOpR-B_38WPSxDqSDdM?usp=sharing
References:
1. Bagnell, Boots: https://homes.cs.washington.edu/~bboots/RL-Fall2020/Lectures/LQR_notes.pdf
2. Russ Tedrake's lecture: https://underactuated.mit.edu/lqr.html
Check out the full series "Core Concept in Robotics": https://www.youtube.com/playlist?list=PLQZQ7N26C6bbhXHuongGbbeVLl-PKkJ6K
This video describes the core component of optimal control, developing the optimization algorithm for solving for the optimal control values assuming one has ac...
This video describes the core component of optimal control, developing the optimization algorithm for solving for the optimal control values assuming one has access to all state variables.
This video describes the core component of optimal control, developing the optimization algorithm for solving for the optimal control values assuming one has access to all state variables.
In this video we introduce the linear quadratic regulator (LQR) controller. We show that an LQR controller is a full state feedback controller where the gain matrix K is computed by solving an optimization problem.
Topics and time stamps:
0:00 - Introduction
4:01 - Introduction to Optimization
19:24 - Setting up the cost function (Q and R matrices)
41:36 - Solving the Algebraic Ricatti Equation
1:01:05 - Example of LQR in Matlab
1:17:09 - Using LQR to address practical implementation issues with full state feedback controllers
In addition, some videos that may be helpful or relevant to the discussion at hand include:
-Introduction to Full State Feedback Control (https://youtu.be/1zIIcYfp5QA)
-Practical Implementation Issues with a Full State Feedback Controller (https://youtu.be/9vCTokJ5RQ8)
-Introduction to Linear State Estimation (TBD)
The Matlab/Simulink files associated with this video are located at:
-http://faculty.washington.edu/lum/EducationalVideoFiles/Controls05/LQRIntro.nb
-http://faculty.washington.edu/lum/EducationalVideoFiles/Controls05/IntroToLQR.m
-http://faculty.washington.edu/lum/EducationalVideoFiles/Controls05/IntroToLQR_model.slx
You can support this channel via Patreon at https://www.patreon.com/christopherwlum. Thank you for your help!
This video shows how to use LQR controller to enforce a state in a given dynamic system (state space) to track a desired reference rather than be regulated to zero.
Also watch:
LQG controller for tracking a given reference! An example in Matlab: https://youtu.be/7v7U-ydCWV8
Type 1 Servo (reference tracking) for Plants with no Integrator – Inverted pendulum with MATLAB Code: https://youtu.be/PlWwDDyQ7C0
Here we design an optimal full-state feedback controller for the inverted pendulum on a cart example using the linear quadratic regulator (LQR). In Matlab, we find that this is a simple one-line command 'lqr'.
These lectures follow Chapter 8 from:
"Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Brunton and Kutz
Amazon: https://www.amazon.com/Data-Driven-Science-Engineering-Learning-Dynamical/dp/1108422098
Chapters available at: http://databookuw.com/databook.pdf
Book Website: http://databookuw.com
Brunton Website: eigensteve.com
This video was produced at the University of Washington
The robot is able to maneuver in any terrain with balancing it self on 2 wheels
We #design #products with #research and #excellence so that you can #commit to your #customers.
- Electronics prototyping
- Embedded systems design
- Robotics and automation
- Internet of Things (IoT) solutions
- Circuit and PCB design
- Firmware development
Visit: ardralab.com
#controltheory #robotics #controlengineering #mechatronics #machinelearning #electricalengineering #signalprocessing #python #pythontutorial #signals_systems #machinelearning #optimization #robotics #electricalengineering #mechanicalengineering #autonomoussystems #robots #simulation #optimalcontrol
It takes a significant amount of time and energy to create these free video tutorials. You can support my efforts in this way:
- Buy me a Coffee: https://www.buymeacoffee.com/AleksandarHaber
- PayPal: https://www.paypal.me/AleksandarHaber
- Patreon: https://www.patreon.com/user?u=32080176&fan_landing=true
- You Can also press the Thanks YouTube Dollar button
In this control systems and control engineering tutorial, we explain how to implement the linear quadratic regulator (LQR) in Python. We use a mass spring damper system as a test case. We design an LQR controller for non-zero set points. This means that we need to design a steady-state desired control inputs. We use the Python Control Systems Library to implement the LQR controller.
We explore the concept of control in robotics, notably Linear Quadratic Regulators (LQR). We see that a powerful way to think about control is as a dynamic optimization, where the goal is to compute a mapping from states to action that minimize a user-specified cost, e.g. land a rocket without exploding. Moreover, you can do this efficiently thanks to Bellman’s insight that the optimal value of a state at your current time is simply the minimum one-step cost + the optimal value at the next time, suggesting an elegant iterative procedure. What makes LQR special is that you can do this analytically because the optimal value function is a quadratic! This property grants LQR a first-class status as theoretically fundamental and practically powerful.
Notebook for LQR applied to inverted pendulum: https://colab.research.google.com/drive/1ofAmq3M4lMMXfSOpR-B_38WPSxDqSDdM?usp=sharing
References:
1. Bagnell, Boots: https://homes.cs.washington.edu/~bboots/RL-Fall2020/Lectures/LQR_notes.pdf
2. Russ Tedrake's lecture: https://underactuated.mit.edu/lqr.html
Check out the full series "Core Concept in Robotics": https://www.youtube.com/playlist?list=PLQZQ7N26C6bbhXHuongGbbeVLl-PKkJ6K
This video describes the core component of optimal control, developing the optimization algorithm for solving for the optimal control values assuming one has access to all state variables.
In this video we introduce the linear quadratic regulator (LQR) controller. We show that an LQR controller is a full state feedback controller where the gain matrix K is computed by solving an optimization problem.
Topics and time stamps:
0:00 - Introduction
4:01 - Introduction to Optimization
19:24 - Setting up the cost function (Q and R matrices)
41:36 - Solving the Algebraic Ricatti Equation
1:01:05 - Example of LQR in Matlab
1:17:09 - Using LQR to address practical implementation issues with full state feedback controllers
In addition, some videos that may be helpful or relevant to the discussion at hand include:
-Introduction to Full State Feedback Control (https://youtu.be/1zIIcYfp5QA)
-Practical Implementation Issues with a Full State Feedback Controller (https://youtu.be/9vCTokJ5RQ8)
-Introduction to Linear State Estimation (TBD)
The Matlab/Simulink files associated with this video are located at:
-http://faculty.washington.edu/lum/EducationalVideoFiles/Controls05/LQRIntro.nb
-http://faculty.washington.edu/lum/EducationalVideoFiles/Controls05/IntroToLQR.m
-http://faculty.washington.edu/lum/EducationalVideoFiles/Controls05/IntroToLQR_model.slx
You can support this channel via Patreon at https://www.patreon.com/christopherwlum. Thank you for your help!
This video shows how to use LQR controller to enforce a state in a given dynamic system (state space) to track a desired reference rather than be regulated to zero.
Also watch:
LQG controller for tracking a given reference! An example in Matlab: https://youtu.be/7v7U-ydCWV8
Type 1 Servo (reference tracking) for Plants with no Integrator – Inverted pendulum with MATLAB Code: https://youtu.be/PlWwDDyQ7C0
Here we design an optimal full-state feedback controller for the inverted pendulum on a cart example using the linear quadratic regulator (LQR). In Matlab, we find that this is a simple one-line command 'lqr'.
These lectures follow Chapter 8 from:
"Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Brunton and Kutz
Amazon: https://www.amazon.com/Data-Driven-Science-Engineering-Learning-Dynamical/dp/1108422098
Chapters available at: http://databookuw.com/databook.pdf
Book Website: http://databookuw.com
Brunton Website: eigensteve.com
This video was produced at the University of Washington
The robot is able to maneuver in any terrain with balancing it self on 2 wheels
We #design #products with #research and #excellence so that you can #commit to your #customers.
- Electronics prototyping
- Embedded systems design
- Robotics and automation
- Internet of Things (IoT) solutions
- Circuit and PCB design
- Firmware development
Visit: ardralab.com
#controltheory #robotics #controlengineering #mechatronics #machinelearning #electricalengineering #signalprocessing #python #pythontutorial #signals_systems #machinelearning #optimization #robotics #electricalengineering #mechanicalengineering #autonomoussystems #robots #simulation #optimalcontrol
It takes a significant amount of time and energy to create these free video tutorials. You can support my efforts in this way:
- Buy me a Coffee: https://www.buymeacoffee.com/AleksandarHaber
- PayPal: https://www.paypal.me/AleksandarHaber
- Patreon: https://www.patreon.com/user?u=32080176&fan_landing=true
- You Can also press the Thanks YouTube Dollar button
In this control systems and control engineering tutorial, we explain how to implement the linear quadratic regulator (LQR) in Python. We use a mass spring damper system as a test case. We design an LQR controller for non-zero set points. This means that we need to design a steady-state desired control inputs. We use the Python Control Systems Library to implement the LQR controller.
We explore the concept of control in robotics, notably Linear Quadratic Regulators (LQR). We see that a powerful way to think about control is as a dynamic optimization, where the goal is to compute a mapping from states to action that minimize a user-specified cost, e.g. land a rocket without exploding. Moreover, you can do this efficiently thanks to Bellman’s insight that the optimal value of a state at your current time is simply the minimum one-step cost + the optimal value at the next time, suggesting an elegant iterative procedure. What makes LQR special is that you can do this analytically because the optimal value function is a quadratic! This property grants LQR a first-class status as theoretically fundamental and practically powerful.
Notebook for LQR applied to inverted pendulum: https://colab.research.google.com/drive/1ofAmq3M4lMMXfSOpR-B_38WPSxDqSDdM?usp=sharing
References:
1. Bagnell, Boots: https://homes.cs.washington.edu/~bboots/RL-Fall2020/Lectures/LQR_notes.pdf
2. Russ Tedrake's lecture: https://underactuated.mit.edu/lqr.html
Check out the full series "Core Concept in Robotics": https://www.youtube.com/playlist?list=PLQZQ7N26C6bbhXHuongGbbeVLl-PKkJ6K
This video describes the core component of optimal control, developing the optimization algorithm for solving for the optimal control values assuming one has access to all state variables.
The .LBR file format was an archive file format used on CP/M and DOS operating systems during the early 1980s. .LBR files were created by the LU program; later programs like NULU arrived for .LBR creation, and many tools such as LT and QL were capable of extracting from .LBR archives. .LBR is an abbreviation of "Library", and, resembling the .tar file format, member files were only stored in the .LBR file, not compressed. As transfer of LBR files by modem was common, it was typical practice for archiving a collection of files to compress them using the SQ or CRUNCH programs then store them in an .LBR archive, or else (more rarely) store the files in the LBR archive, then use SQ or CRUNCH to compress the archive. A compressed LBR archive file was given the extension ".LQR" (if squeezed) or ".LZR" (if crunched); however, it was more common to compress the members of the archive than to compress the archive as a whole.
As MS-DOS and other operating systems became more popular and displaced CP/M, .LBR's popularity waned. The development of the ARC archiver which both compressed and archived files in one program went a long way towards displacing .LBR on MS-DOS systems; on CP/M systems, .LBR persisted longer due to the lack of a useful ARC port.
Lecrae: All s- All s- (The boy is dangerous) All saved, all serious All saved, all serious All saved, all s- All saved, all s- All saved, all serious Verse: Yo clicked up 40 deep in the street you can find us Ya we on theology but we be on the grind ya When we was a youngers only had 2 place to run to One become an animal, two get out the jungle So we got our lion on the line bro, that's what we do Run up on you and your crew and tell ya'll Jesus is the truth Open air evangelists, relationships we do it all Backpack still full of tracks with a Johnny Mac, hats to the back and our backs to the wall Plus I got some homies out there who gon' rep the rock If you wanna make them stop, you gon' have to bring a choppa If they get martyred, then we gon' go harder Share the gospel on death row and let 'em know that they been pardoned You don't wanna get it started, this is what we do, who we are 1-1-6 to the day we die, ain't tryin to be no superstars Chicks to the click that'll pull your car? Usin' the street like cops in cars? You ain't hear the truth today, I promise we'll be back tomorrow Hook: Clicked up 40 deep, all saved, all serious Clicked up 40 deep, hey holla at us if you curious Clicked up 40 deep, we all saved, all serious Clicked up 40 deep, come holla at us if you curious Clicked up 40 deep, all saved, all serious (Yah) Clicked up 40 deep, all saved, all serious (Yah, it's community baby, haha) Clicked up 40 deep, all saved, all serious (Let's take it back, Creezie, let's talk about it) Clicked up 40 deep, all saved, all serious (Let me show you Tedashii's style, baby) Tedashii: To the streets, like Crae Clicked up 40 deep, all day Backpacks they strap 'em Johnny Mac and tracks to play for me this morning to label the streets, all day (okay) 1-1-6 for your boy, man this morning just some ?? Clicked up in community, baby, this here the ?? Trip Lee: A clique of us is shining rhyming walking talking sharing Christ, very hype Find us hiding behind him all prepared to fight, very tight Fighting trying to share the cross. He spared of life We're living by the blood like we're parasites, get it right My team carries bunch of high beam blaring lights Might seem scary but we nice, see we carry life Light is seen clearly man we're glaring very bright Check the fleet man we deep so we might seem Barry White Ever since we heard about the murder how they buried Christ Eyes upon the cross even though that is a scary sight But that was the merger we converted now we very tight He died for His bride homey, How you like the married life? Christ the name we calling on Cant wait til He calls us home You know we be falling often we cant walk it all alone My crews always rhyming like some stalkers we aint stalkin homes'
("LQR", "LQR House", or the "Company"), intends to become a prominent force in the wine and spirits e-commerce, sector epitomized by its flagship alcohol marketplace, CWSpirits.com ("CWSPlatform").
) Ownership SubmissionFORM 4. Check this box if no longer subject to Section 16. Form 4 or Form 5 obligations may continue. See Instruction 1(b) ... See Instruction 10. UNITED STATES SECURITIES AND EXCHANGE COMMISSION ... 2 ... LQRHouseInc. [LQR] 5 ... LQR House Inc.
("LQR", "LQR House", or the "Company"), intends to become a prominent force in the wine and spirits e-commerce, sector epitomized by its flagship alcohol marketplace, CWSpirits.com ("CWSPlatform").
LQRHouseInc ...MIAMI BEACH, FL / ACCESSWIRE / December 20, 2024 / LQR House Inc ... These appointments mark a pivotal step in LQR House's strategic growth, as both bring extensive leadership, innovation, and financial expertise to the Company.