Applied Control Systems 3: UAV drone (3D Dynamics & control) from Udemy

One of the greatest transformations that we will see in the next couple of decades is going to be the advent of autonomous drones. While being used extensively already, the applications of quadcopters will only grow in time. Drones will be used in delivery services, entertainment, medicine, military, rescue, structural quality inspection - places that people cannot reach easily, and in many other fields.

In many cases, there will be a predefined trajectory in a 3D space that the UAV needs to follow without human help. In fact, humans might simply give a simple command for the drone to go somewhere, and then, a specific trajectory will be generated by a computer in that direction and the UAV's control algorithms will need to determine EXACTLY how fast each rotor should turn in order to make the drone follow that trajectory with high-degree precision.

And that's what this course is all about - its about

In this course, you will receive a full package when it comes to learning about how to model and control a UAV drone and make it follow a trajectory in a 3D environment. Not only you will learn how to model a UAV system mathematically by deriving the equations of motion using the principles of 3D Dynamics, but you will also be exposed to some of the most powerful control techniques out there such as Model Predictive Control and feedback linearization.

In 3D dynamics, you will learn the fundamental math and physics behind the UAV quadcopter drone modelling. You will learn how to describe the position and orientation of a UAV quadcopter drone in a 3D space using rotation and transfer matrices, Newton - Euler 6 Degree of Freedom equations of motion, widely used Runge - Kutta integrator in engineering and propeller dynamics.

In the end of the course, I will also explain to you the code in the Python simulator.

Understanding the material in this course fundamentally, being able to quantify it mathematically, and knowing how to apply it using coding - that will give you an advantage in your engineering career that you cannot even imagine yet. It will give you a competitive edge that you need in the labor market.

I'm very excited to start working with you. Take a look at some of my free preview videos, and if you like what you see, then ENROLL in the course, and let's get started right now.

What's inside

Learning objectives

Mathematical modelling of a uav quadcopter drone
Obtaining kinematic equations: rotation & transfer matrices
Obtaining newton-euler 6 dof dynamic equations of motion with rotating frames
Going from equations of motion to a uav specific state-space equations
Understanding the gyroscopic effect & applying it to the uav model

Understanding the runge-kutta integrator and applying it to the uav model
Mastering & applying model predictive control algorithm to the uav
Mastering & applying a feedback linearization controller to the uav
Combining model predictive control and feedback linearization in one global controller
Simulating the drone's trajectory tracking in python using the mpc and feedback linearization controller

Mathematical modelling of a uav quadcopter drone
Obtaining kinematic equations: rotation & transfer matrices
Obtaining newton-euler 6 dof dynamic equations of motion with rotating frames
Going from equations of motion to a uav specific state-space equations
Understanding the gyroscopic effect & applying it to the uav model
Understanding the runge-kutta integrator and applying it to the uav model
Mastering & applying model predictive control algorithm to the uav
Mastering & applying a feedback linearization controller to the uav
Combining model predictive control and feedback linearization in one global controller
Simulating the drone's trajectory tracking in python using the mpc and feedback linearization controller

Syllabus

You will learn relevant drone architecture necessary for control engineering

Introduction

UAV configuration + inertial VS body frame

Inputs and outputs of a 6 Degree of Freedom UAV drone

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Taught by Mark Misin Engineering Ltd, who are recognized for their work in Mechatronics and Controls

Provides hands-on virtual simulation

Builds a strong foundation for understanding UAVs

Develops fundamental and advanced skills in UAV modeling and control relevant to industry and research

Involves advanced mathematical concepts and complexities

Requires extensive background knowledge in mathematics and physics

Reviews summary

In-depth uav dynamics & control

According to learners, this course offers an incredibly thorough and detailed deep dive into UAV drone dynamics and advanced control techniques like Model Predictive Control and feedback linearization. Students commend the instructor's clear explanations and passion for the subject. While the course provides a robust theoretical foundation complemented by a valuable Python simulation for practical application, a notable point of discussion is the high prerequisite knowledge required, particularly in advanced mathematics. Some found the pace dense and desired more integrated practical exercises, indicating it's best suited for those with a strong engineering background.

Includes a valuable Python simulator for applying theoretical concepts.

"The Python simulation at the end was a fantastic way to apply everything."

"The Python code was helpful, though I wish there were more practical exercises or projects throughout the course..."

"The Python simulator is a good addition. However, the lack of interactive assignments or quizzes meant I had to create my own problems to practice."

Instructor clearly explains complex concepts with passion.

"The instructor explains complex concepts clearly. I particularly appreciated the detailed explanation of MPC and feedback linearization."

"The instructor's passion for the subject shines through. The lectures are dense but comprehensive."

"I feel the instructor clearly knows his stuff. The mathematical rigor is high, which is good for an engineer."

Provides an extensive and detailed foundation in UAV dynamics.

"This course is incredibly thorough and well-structured, especially the derivations of the 3D dynamics equations."

"Absolutely brilliant! The level of detail on UAV dynamics and control algorithms like MPC is unparalleled."

"I found the course provided an excellent theoretical foundation, and the explanations of 6 DOF dynamics and various control techniques were thorough."

Information is dense; some desire more integrated exercises.

"The pace can be overwhelming. I struggled with some of the mathematical derivations and wished there were more step-by-step examples."

"I found the Python code at the end felt a bit rushed after so much theory. It's definitely for advanced learners."

"I found the lack of interactive assignments or quizzes meant I had to create my own problems to practice, which was a bit time-consuming."

Requires a strong background in advanced mathematics and physics.

"My background in advanced calculus helped immensely. This is not for beginners in control systems."

"The course assumes a strong foundation in linear algebra and differential equations, which is fair given the topic."

"Way too much theory and math for me. It's not very accessible for someone without a strong aerospace or mechanical engineering background."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Applied Control Systems 3: UAV drone (3D Dynamics & control) with these activities:

Review basic linear algebra

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Review basic linear algebra to strengthen your mathematical foundation for the course.

Browse courses on Linear Algebra

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Go over your notes from a previous linear algebra course.
Solve practice problems.
Take an online refresher course.
Watch video tutorials on linear algebra.

Mentor other students in the course

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Offer support and guidance to other students in the course to reinforce your own understanding of the material and help others succeed.

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Identify a student who is struggling with the material.
Offer to help the student by answering their questions or providing additional explanations.
Meet with the student regularly to provide support and guidance.

Review the book 'Control of Nonlinear Systems' by Khalil

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Read the book 'Control of Nonlinear Systems' by Khalil to gain a deeper understanding of nonlinear control theory and its applications.

View Control Systems: An Introduction on Amazon

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Purchase or borrow the book.
Read the book carefully, taking notes as you go.
Try to solve the exercises at the end of each chapter.

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Solve Newton-Euler equations

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Practice solving Newton-Euler equations to reinforce your understanding of rigid body dynamics and prepare for the course.

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Review the equations of motion for a rigid body.
Choose a simple rigid body system.
Apply the Newton-Euler equations to the system to determine its motion.

Follow a tutorial on feedback linearization

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Watch a tutorial on feedback linearization to enhance your understanding of the technique and improve your ability to design controllers for nonlinear systems.

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Find a tutorial on feedback linearization that is appropriate for your skill level.
Watch the tutorial and take notes.
Try to apply the technique to a simple nonlinear system.

Create a video tutorial on the use of the Runge-Kutta integrator

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Create a video tutorial on the use of the Runge-Kutta integrator to reinforce your understanding of numerical methods and improve your ability to solve differential equations.

Browse courses on Numerical Methods

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Review the theory behind the Runge-Kutta integrator.
Choose a software package for creating the video tutorial.
Record the video tutorial, explaining the concepts clearly and concisely.

Attend a workshop on Model Predictive Control

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Attend a workshop on Model Predictive Control to learn about the technique and how to apply it to real-world problems.

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Find a workshop on Model Predictive Control that fits your schedule and interests.
Register for the workshop.
Attend the workshop and participate actively.

Build a simple quadcopter drone

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Build a simple quadcopter drone to apply your knowledge of control systems and robotics and gain hands-on experience.

Browse courses on Robotics

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Gather the necessary materials.
Assemble the drone.
Program the drone to fly.

Career center

Learners who complete Applied Control Systems 3: UAV drone (3D Dynamics & control) will develop knowledge and skills that may be useful to these careers:

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Applied Control Systems 3

UAV drone (3D Dynamics & control)

What's inside

Learning objectives

Syllabus

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Reviews summary

In-depth uav dynamics & control

Activities

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