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

Modeling + state space systems + Model Predictive Control + feedback control + Python simulation: UAV quadcopter drone

Created by: Mark Misin Engineering

Last updated November 2025

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What you will learn

  • 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

How it compares

Against the other 335 rated Udemy courses in Engineering on CourseDuck:

  • Rating4.6 out of 5higher than 74% of them
  • Reviews584#135 of 336 by number of reviews
  • Length27.7 hourslonger than 95% of them
  • Last updatedNovember 2025more recently than 69% of them

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Course Description

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 DESIGNING, MASTERING, and APPLYING these control algorithms together with deriving the dynamics equations for the quadcopter.

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!

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Instructor Details

Mark Misin Engineering

The content in control systems, engineering mechanics, mathematical modeling, and Python is often challenging to learn, however, it's indispensable in engineering.

Fortunately, with intuitive teaching and practical applications, it can be made a lot easier and much more exciting to study.

I'm here to do just that. I want to give you strong fundamentals in those topics, so that you could thrive in your studies and career.

Mark Misin
Aerospace & Robotics engineer



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Reviews

4.6

584 ratings on Udemy

Select a bar to show only those reviews.Select the bar again to show every rating.

By Samira Taghilooniya on 8/31/2026

I am new to the field of control, and I am really enjoying this course. The explanations are very clear, and it is evident that there is a great deal of knowledge and experience behind the course. Since English is not my native language, I also really appreciate how clear and easy to understand the accent is.

By Mahmoud Chehoury on 6/24/2026

Loss of animation and dynamics

By Ankit Choudhary on 3/29/2026

I like the course in all its aspects, every topic was explained with clarity, complete in itself as all the material, math's is explained in simple intuitive manner. Implantation of all concepts in python was really helpful for application-oriented understanding and bringing theory to practical.

By Vinicius Freitas Rodrigues on 4/10/2025

Excellent course! The topics regarding mathematical modeling were taught impeccably. I also learned a great deal from the topics on control, as I had never encountered nonlinear control before. As a researcher, I am studying your master's thesis and I find the LPV-MPC architecture interesting. However, I truly believe it may not have been the best idea to include it in a course that will, for many, serve as their first contact with UAV control.

By Venelin Stoyanov on 10/30/2024

The course was absolutely fantastic. I learned many new things and refreshed others. Mark is very knowledgeable and cares about student questions which he answers promptly. I think student must make good notes so it can refer back to the previous lectures. He provides proofs of all the equation and math models . The three Automatic control courses compliment each other must be done in order to have complete grasp of the material. May be in the future Mark can create course for Kalman filters which is delivered in the same easy to understand teaching style. Thank you Mark for sharing your knowledge.

By Michael on 6/27/2022

This Course explained a few things in detail, i always struggled to understand due to many reasons (lazyness to research etc.), but finally I am able to understand why it is useful to derive some equations with the Euler forumlation. So for me it was very helpful. Thank you

By Nitin Murugan on 3/1/2022

Fantastic course ! The explanations are detailed and clear. I would've liked to see more assignments where we get to play with the code. Like small coding assignment at the end of every section where the student can do a bit of exploring, such as with rotation matrices, generating a simple trajectory, applying MPC to a simple math problem, maybe validate different parts of the code etc. Otherwise, I'm very happy with the presentation and I look forward to learn more from Mark !

By Siddhesh Sawant on 7/18/2021

Great Course, explaining the Control Architecture of a 3D Robot with emphasis on Mathematics, Physics and Coding. Finding really helpful and self contained courses is quite rare, and this is surely one of those great courses. Looking forward to exciting career opportunities after course completion as a Control Software Developer.

By Micael Menendez on 3/30/2021

Hard to follow hand writting. Concepts are not clearly explained and it goes to deep into mathematical analysis that are not that important for a GNC course.

By Jeffrey Mays on 3/29/2021

This class goes through the basics and provides great examples. As an aerospace engineer, this was a great refresher, and the simulation code alone is worth it for learning MPC and quadcopter dynamics. Great class! The teacher should be proud of their hard work.

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Overall Score : 92 / 100