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

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

4.61 (347 reviews)
Udemy
platform
English
language
Engineering
category
5,648
students
27.5 hours
content
May 2023
last update
$79.99
regular price

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

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!

Content

Drone architecture from Control Systems point of view

Introduction + General recap
UAV configuration + inertial VS body frame
Inputs and outputs of a 6 Degree of Freedom UAV drone
Propeller rotation directions 1
Propeller rotation directions 2 - Helicopter example
1st control action - Thrust
2nd control action - Roll
3rd control action - Pitch (exercise)
3rd control action - Pitch (solution) + 4th control action - Yaw (exercise)
4th control action - Yaw (solution)
Rotation vector direction
Global view of the drone's control architecture
Follow up!

Fundamental kinematics & dynamics equations for a 6 DOF system (Newton - Euler)

Kinematics VS Dynamics
Measuring the UAV's position (exercise)
Measuring the UAV's position (solution)
Intro to describing attitudes 1 (exercise)
Intro to describing attitudes 2 (solution + new exercise)
2D rotation matrix formulation (solution + new exercise)
From 2D to 3D rotations (solution + new exercise)
3D rotation matrix formulation about the Z axis 1 (solution)
3D rotation matrix formulation about the Z axis 2 (solution)
Projecting from 3D to 2D (exercise)
Projecting from 3D to 2D (solution) + constructing Rx and Ry matrices (exercise)
Constructing Ry matrix (solution)
Constructing Rx matrix (solution)
Orthonormal matrices (exercise)
Orthonormal matrices (solution)
3D rotation sequence 1 (exercise)
3D rotation sequence 2 (solution)
Guide on extra information on rotation matrix mathematical derivation
Intro to Euler angles (rotation about moving body frames)
Intuition on different conventions
Fixed VS Moving body frame rotations 1 (exercise)
Fixed VS Moving body frame rotations 2 (solution + new exercise)
Fixed VS Moving body frame rotations 3 (solution)
Rotation matrix convention used in this course
Rotation matrix application to the UAV 1
Rotation matrix application to the UAV 2
Transfer matrix derivation 1
Transfer matrix derivation 2
Transfer matrix derivation 3 (exercise)
Transfer matrix derivation 3 (solution + new exercise)
Mathematical derivation of the Rzyx (moving frame) rotation matrix
Transfer matrix derivation 4 (solution)
Transfer matrix derivation 5
Rotation & Transfer matrix application 1 - Kinematics wrap up
Rotation & Transfer matrix application 2 - Kinematics wrap up
Intro to Dynamics
Dot product 1 + Application
Dot product 2 +Application
Dot product 3 + Application (exercise)
Dot product 4 + Application (solution)
Cross Product 1
Cross Product 2 (Exercise)
Cross Product 3 (Solution)
Cross Product Application 1
Cross Product Application 2 (exercise)
Cross Product Application 2 (Solution)
Mass moments of inertia & inertia tensor 1
Mass moments of inertia & inertia tensor 2 (exercise)
Mass moments of inertia & inertia tensor 3 (solution)
Mathematical formulas of mass moments of inertia
Mathematical formulas of products of inertia
Principal axis
Dynamics: Translational Motion (Inertial Frame)
Dynamics: Translational Motion (Body Frame) 1
Dynamics: Translational Motion (Body Frame) 2
Dynamics: Translational Motion (Body Frame) 3
Angular momentum VS angular velocity 1
Angular momentum VS angular velocity 2
Dynamics: Rotational Motion (Inertial frame)
Dynamics: Rotational Motion (Body frame) 1
Dynamics: Rotational Motion (Body frame) 2
Autonomous vehicle lateral acceleration through new lenses
Dynamics: Rotational Motion (Body frame) - alternative form (exercise)
Dynamics: Rotational Motion (Body frame) - alternative form (solution)

Specific UAV plant model

From 6 DOF Newton-Euler to state-space (exercise)
From 6 DOF Newton-Euler to state-space (solution)
Applying Force of gravity to the UAV (exercise)
Applying Force of gravity to the UAV (solution)
Applying control inputs to the UAV (exercise)
Gyroscopic effect intuition 1 + control inputs (solution)
Gyroscopic effect intuition 2 (exercise)
Gyroscopic effect intuition 3 (solution)
Gyroscopic effect on a UAV intuition 1 (exercise)
Gyroscopic effect on a UAV intuition 2 (solution)
Gyroscopic effect on a UAV intuition 3
Gyroscopic effect on a UAV - Math 1 (exercise)
Gyroscopic effect on a UAV - Math 2 (solution)
Gyroscopic effect on a UAV - Math 3
Gyroscopic effect on a UAV - Math 4
From 6 DOF Newton-Euler to state-space - Math 1 (exercise)
From 6 DOF Newton-Euler to state-space - Math 2 (solution)
UAV plant model schematics 1 (exercise)
UAV plant model schematics 2 (solution)
Euler state integrator
Runge - Kutta integrator 1
Runge - Kutta integrator 2
Runge - Kutta integrator 3
Runge - Kutta integrator 4
Runge - Kutta integrator 5
Runge - Kutta integrator 6
Runge - Kutta integrator 7
Runge - Kutta integrator 8
From control inputs to rotor angular velocities - blade element theory 1
From control inputs to rotor angular velocities - blade element theory 2
From control inputs to rotor angular velocities - blade element theory 3
From control inputs to rotor angular velocities - blade element theory 4
From control inputs to rotor angular velocities - blade element theory 5
From control inputs to rotor angular velocities - blade element theory 6
From control inputs to rotor angular velocities - blade element theory 7
From control inputs to rotor angular velocities - blade element theory 8
From control inputs to rotor angular velocities - blade element theory 9
From control inputs to rotor angular velocities - blade element theory 10
From control inputs to rotor angular velocities - blade element theory 11
From control inputs to rotor angular velocities - blade element theory 12
From control inputs to rotor angular velocities - blade element theory 13

Recap of Applied Control Systems for Engineers 1 - autonomous vehicle

Detailed recap 1: car & bicycle lateral equations of motion
Detailed recap 2: LTI state - space equations
Detailed recap 3: continuous VS discrete LTI
Detailed recap 4: system input calculation using Model Predictive Control

The UAV's global control architecture

The global control architecture scheme - Intro
The elements of the sequential/cascaded controller
Different tasks of each sub-controller
The Planner
Stronger VS weaker dynamics 1
Stronger VS weaker dynamics 2
Reference trajectory equations in the planner
The affect of the control inputs on future states

The MPC attitude controller

Review of the global control structure
Review of the state space equations of the autonomous vehicle
The UAV's dynamics and kinematics equations revisited
Small angle roll and pitch assumption 1
Small angle roll and pitch assumption 2
Putting the state space equations in the Linear format 1
Putting the state space equations in the Linear format 2
Putting the state space equations in the Linear format 3
Putting the state space equations in the Linear format 4
Linear Parameter Varying form 1
Linear Parameter Varying form 2
Review of the steps from the equations of motion to the plant
The dimensions of the state space equation matrices
Future state prediction formula 1
Future state prediction formula 2
Future state prediction formula 3
Cost function 1
Cost function 2
Cost function 3
Cost function 4
Cost function 5
Cost function 6
Cost function 7
Cost function 8
Cost function 9
Cost function 10
Cost function 11

Feedback Linearization Controller

Equations of motion for position control (inertial frame) - exercise
Equations of motion for position control (inertial frame) - solution
General feedback control architecture
Feedback Linearization Controller schematics - Part 1
Differential Equations - intro
Differential Equations & the control law
Solving differential equations - real roots 1
Solving differential equations - real roots 2
Solving differential equations - real roots 3
Solving differential equations - complex roots 1
Solving differential equations - complex roots 2
Solving differential equations - complex roots 3
Solving differential equations - complex roots 4
Using the exponent for controlling a system - exercise
Using the exponent for controlling a system - solution
Poles & Laplace domain
From poles to differential equation constants - exercise
From poles to differential equation constants - solution
From differential equations to state-space representation
Eigenvalues in control engineering & Determinants
Computing eigenvectors
Laplace VS Fourier frequency domain
Moving poles
Feedback Linearization Controller schematics - Part 2
Simulation results with real & complex poles 1
Simulation results with real & complex poles 2
Simulation results with real & complex poles 3
Feedback Linearization Controller schematics - Part 3
Final Stretch - computing the final control inputs - Part 1
Final Stretch - computing the final control inputs - Part 2

The simulation code explanation

MUST HAVE Matplotlib 3.2.2, NOT Matplotlib 3.3.3
Python installation instructions - Ubuntu
Python installation instructions - Windows 10
Simulation analysis & code explanation 1
Simulation analysis & code explanation 2
Simulation analysis & code explanation 3
Simulation analysis & code explanation 4
Simulation analysis & code explanation 5
Simulation analysis & code explanation 6
Simulation analysis & code explanation 7
Simulation analysis & code explanation 8
Simulation analysis & code explanation 9
Simulation analysis & code explanation 10
Simulation analysis & code explanation 11
Simulation analysis & code explanation 12
Simulation analysis & code explanation 13
Thank You!
Python codes & course summary document

Last Words

INTUITION MATTERS! Applied Calculus for Engineers - Complete

Screenshots

Applied Control Systems 3: UAV drone (3D Dynamics & control) - Screenshot_01Applied Control Systems 3: UAV drone (3D Dynamics & control) - Screenshot_02Applied Control Systems 3: UAV drone (3D Dynamics & control) - Screenshot_03Applied Control Systems 3: UAV drone (3D Dynamics & control) - Screenshot_04

Reviews

Ron
June 23, 2023
Mechatronics systems is my favorite subject.This includes diving to the dynamics analysis,kinematics and control design of autonomous ground vehicles,drown,robots etc.. This series of courses present the subjects thar I love from all engineering points of view.
Michael
June 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
Muhammed
April 28, 2022
Currently working on my Master's in Electrical Engineering on UAV swarming and believe me this is gonna help me a lot
Panya
March 9, 2022
The course detail is very very clear. The teacher pronounciation and voice are very clear. The teaching speed is good. Very easy to understand. So far so good.
Afner
March 2, 2022
Really clear in explaining fundamental concept and really helpful for me to understand how to design controll system for UAV. Thankyou so much.
Nitin
March 2, 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 !
Juan
December 30, 2021
Really great course to learn how a UAV system is modelled and computed. I strongly recommend this course to engineers interested in Drones and system automatizations. I learnt a lot and had fun with the course. Congratulations for the course.
Dimple
November 10, 2021
Amazing course. You will need to properly pay attention to topics but once you do, and follow through all the lectures without skipping parts you will develop very strong fundamentals.
Prachit
September 28, 2021
Fantastic course! I like how math was explained to every little detail. A suggestion might to get to write the code right away, as we finish each section (plant, controllers etc). Thank you!
Mahmoud
September 18, 2021
This course is great, will learn you a lot of things you ever never know before. i will give this course 100000000000 stars
Andrew
August 19, 2021
The course is well structured and explanations are great. Mark is a great teacher, one that inspires! I learnt quite a lot about drone dynamics and control!
Siddhesh
July 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.
Andres
May 25, 2021
Very well explained all the concepts around the dynamics and control of the drone, particularly for the quadcopter configuration. I am really impressed how Mark makes all this tough knowledge understandable.
Brajesh
May 12, 2021
I learnt so many things about Quadcopter that is extremely useful for my research. My Sincere thanks to Dr. Mark Misin.
Jeffrey
March 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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3357352
udemy ID
7/23/2020
course created date
12/18/2020
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