Applied Control Systems 3: Uav Drone (3D Dynamics & Control)
Last updated 8/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 10.96 GB | Duration: 27h 22m

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

What you'll 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

Requirements
Basic Calculus: Functions, Derivatives, Integrals
Vector-Matrix multiplication
Udemy course: Applied Control Systems 1: autonomous cars (Math + PID + MPC)

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!

Overview

Section 1: Drone architecture from Control Systems point of view

Lecture 1 Introduction

Lecture 2 UAV configuration + inertial VS body frame

Lecture 3 Inputs and outputs of a 6 Degree of Freedom UAV drone

Lecture 4 Propeller rotation directions 1

Lecture 5 Propeller rotation directions 2 - Helicopter example

Lecture 6 1st control action - Thrust

Lecture 7 2nd control action - Roll

Lecture 8 3rd control action - Pitch (exercise)

Lecture 9 3rd control action - Pitch (solution) + 4th control action - Yaw (exercise)

Lecture 10 4th control action - Yaw (solution)

Lecture 11 Rotation vector direction

Lecture 12 Clarification on measuring with respect to body or inertial frames

Lecture 13 Global view of the drone's control architecture

Lecture 14 Follow up!

Section 2: Fundamental kinematics & dynamics equations for a 6 DOF system (Newton - Euler)

Lecture 15 Kinematics VS Dynamics

Lecture 16 Measuring the UAV's position (exercise)

Lecture 17 Measuring the UAV's position (solution)

Lecture 18 Intro to describing attitudes 1 (exercise)

Lecture 19 Intro to describing attitudes 2 (solution + new exercise)

Lecture 20 2D rotation matrix formulation (solution + new exercise)

Lecture 21 From 2D to 3D rotations (solution + new exercise)

Lecture 22 3D rotation matrix formulation about the Z axis 1 (solution)

Lecture 23 3D rotation matrix formulation about the Z axis 2 (solution)

Lecture 24 Projecting from 3D to 2D (exercise)

Lecture 25 Projecting from 3D to 2D (solution) + constructing Rx and Ry matrices (exercise)

Lecture 26 Constructing Ry matrix (solution)

Lecture 27 Constructing Rx matrix (solution)

Lecture 28 Orthonormal matrices (exercise)

Lecture 29 Orthonormal matrices (solution)

Lecture 30 3D rotation sequence 1 (exercise)

Lecture 31 3D rotation sequence 2 (solution)

Lecture 32 3D rotation sequence - example (exercise)

Lecture 33 3D rotation sequence - example (solution)

Lecture 34 Intro to Euler angles (rotation about moving body frames)

Lecture 35 Intuition on different conventions

Lecture 36 Fixed VS Moving body frame rotations 1 (exercise)

Lecture 37 Fixed VS Moving body frame rotations 2 (solution + new exercise)

Lecture 38 Fixed VS Moving body frame rotations 3 (solution)

Lecture 39 Rotation matrix conventions - Intro

Lecture 40 Rotation matrix conventions - R_XYZ matrix product

Lecture 41 Rotation matrix conventions - R_ZYX matrix product

Lecture 42 Rotation matrix conventions - R_XYX matrix product

Lecture 43 Rotation matrix conventions - R_XYZ vs R_ZYX example

Lecture 44 Rotation matrix conventions - R_XYZ vs R_XYX example

Lecture 45 Rotation matrix application to the UAV 1

Lecture 46 Rotation matrix application to the UAV 2

Lecture 47 Why is a special Transfer matrix needed 1

Lecture 48 Why is a special Transfer matrix needed 2

Lecture 49 Why is a special Transfer matrix needed 3

Lecture 50 Transfer matrix derivation 1 (exercise)

Lecture 51 Transfer matrix derivation 2 (solution + new exercise)

Lecture 52 Mathematical derivation of the Rzyx (moving frame) rotation matrix

Lecture 53 Transfer matrix derivation 4 (solution)

Lecture 54 Transfer matrix derivation 5

Lecture 55 Rotation & Transfer matrix application 1 - Kinematics wrap up

Lecture 56 Rotation & Transfer matrix application 2 - Kinematics wrap up

Lecture 57 Intro to Dynamics

Lecture 58 Dot product 1 + Application

Lecture 59 Dot product 2 +Application

Lecture 60 Dot product 3 + Application (exercise)

Lecture 61 Dot product 4 + Application (solution)

Lecture 62 Cross Product 1

Lecture 63 Cross Product 2 (Exercise)

Lecture 64 Cross Product 3 (Solution)

Lecture 65 Cross Product Application 1

Lecture 66 Cross Product Application 2 (exercise)

Lecture 67 Cross Product Application 2 (Solution)

Lecture 68 Mass moments of inertia & inertia tensor 1

Lecture 69 Mass moments of inertia & inertia tensor 2 (exercise)

Lecture 70 Mass moments of inertia & inertia tensor 3 (solution)

Lecture 71 Mathematical formulas of mass moments of inertia

Lecture 72 Mathematical formulas of products of inertia

Lecture 73 Principal axis

Lecture 74 Mass moment of inertia applied to the UAV

Lecture 75 Dynamics: Translational Motion (Inertial Frame)

Lecture 76 Dynamics: Translational Motion (Body Frame) 1

Lecture 77 Dynamics: Translational Motion (Body Frame) 2

Lecture 78 Dynamics: Translational Motion (Body Frame) 3

Lecture 79 Angular momentum VS angular velocity 1

Lecture 80 Angular momentum VS angular velocity 2

Lecture 81 Dynamics: Rotational Motion (Inertial frame)

Lecture 82 Dynamics: Rotational Motion (Body frame) 1

Lecture 83 Dynamics: Rotational Motion (Body frame) 2

Lecture 84 Autonomous vehicle lateral acceleration through new lenses

Lecture 85 Dynamics: Rotational Motion (Body frame) - alternative form (exercise)

Lecture 86 Dynamics: Rotational Motion (Body frame) - alternative form (solution)

Section 3: Specific UAV plant model

Lecture 87 From 6 DOF Newton-Euler to state-space (exercise)

Lecture 88 From 6 DOF Newton-Euler to state-space (solution)

Lecture 89 Applying Force of gravity to the UAV (exercise)

Lecture 90 Applying Force of gravity to the UAV (solution)

Lecture 91 Applying control inputs to the UAV (exercise)

Lecture 92 Gyroscopic effect intuition + control inputs (solution)

Lecture 93 Gyroscopic effect on a UAV intuition 1 (exercise)

Lecture 94 Gyroscopic effect on a UAV intuition 2 (solution)

Lecture 95 Gyroscopic effect on a UAV - Math 1 (exercise)

Lecture 96 Gyroscopic effect on a UAV - Math 2 (solution)

Lecture 97 Gyroscopic effect on a UAV - Math 3

Lecture 98 Gyroscopic effect on a UAV - Math 4

Lecture 99 From 6 DOF Newton-Euler to state-space - Math 1 (exercise)

Lecture 100 From 6 DOF Newton-Euler to state-space - Math 2 (solution)

Lecture 101 UAV plant model schematics 1 (exercise)

Lecture 102 UAV plant model schematics 2 (solution)

Lecture 103 Euler state integrator

Lecture 104 Runge - Kutta integrator 1

Lecture 105 Runge - Kutta integrator 2

Lecture 106 Runge - Kutta integrator 3

Lecture 107 Runge - Kutta integrator 4

Lecture 108 Runge - Kutta integrator 5

Lecture 109 Runge - Kutta integrator 6

Lecture 110 Runge - Kutta integrator 7

Lecture 111 Runge - Kutta integrator 8

Lecture 112 From control inputs to rotor angular velocities - blade element theory 1

Lecture 113 From control inputs to rotor angular velocities - blade element theory 2

Lecture 114 From control inputs to rotor angular velocities - blade element theory 3

Lecture 115 From control inputs to rotor angular velocities - blade element theory 4

Lecture 116 From control inputs to rotor angular velocities - blade element theory 5

Lecture 117 From control inputs to rotor angular velocities - blade element theory 6

Lecture 118 From control inputs to rotor angular velocities - blade element theory 7

Lecture 119 From control inputs to rotor angular velocities - blade element theory 8

Lecture 120 From control inputs to rotor angular velocities - blade element theory 9

Lecture 121 From control inputs to rotor angular velocities - blade element theory 10

Lecture 122 From control inputs to rotor angular velocities - blade element theory 11

Lecture 123 From control inputs to rotor angular velocities - blade element theory 12

Lecture 124 From control inputs to rotor angular velocities - blade element theory 13

Section 4: Recap of Applied Control Systems 1 - autonomous cars (Math + PID + MPC)

Lecture 125 Detailed recap 1: car & bicycle lateral equations of motion

Lecture 126 Detailed recap 2: LTI state - space equations

Lecture 127 Detailed recap 3: continuous VS discrete LTI

Lecture 128 Detailed recap 4: system input calculation using Model Predictive Control

Section 5: The UAV's global control architecture

Lecture 129 The global control architecture scheme - Intro

Lecture 130 The elements of the sequential/cascaded controller

Lecture 131 Different tasks of each sub-controller

Lecture 132 The Planner

Lecture 133 Stronger VS weaker dynamics 1

Lecture 134 Stronger VS weaker dynamics 2

Lecture 135 Reference trajectory equations in the planner

Lecture 136 The affect of the control inputs on future states

Section 6: The MPC attitude controller

Lecture 137 Review of the global control structure

Lecture 138 Review of the state space equations of the autonomous vehicle

Lecture 139 The UAV's dynamics and kinematics equations revisited

Lecture 140 Zero angle roll and pitch assumption 1

Lecture 141 Zero angle roll and pitch assumption 2

Lecture 142 Putting the state space equations in the Linear format 1

Lecture 143 Putting the state space equations in the Linear format 2

Lecture 144 Putting the state space equations in the Linear format 3

Lecture 145 Putting the state space equations in the Linear format 4

Lecture 146 Linear Parameter Varying form 1

Lecture 147 Linear Parameter Varying form 2

Lecture 148 Review of the steps from the equations of motion to the plant

Lecture 149 The dimensions of the state space equation matrices

Lecture 150 Future state prediction formula 1: simplified LPV-MPC

Lecture 151 Future state prediction formula 2: simplified LPV-MPC

Lecture 152 Future state prediction formula 3: nonsimplified LPV-MPC

Lecture 153 Future state prediction formula 4: nonsimplified LPV-MPC

Lecture 154 Future state prediction formula 5: nonsimplified LPV-MPC

Lecture 155 Cost function 1

Lecture 156 Cost function 2

Lecture 157 Cost function 3

Lecture 158 Cost function 4

Lecture 159 Cost function 5

Lecture 160 Cost function 6

Lecture 161 Cost function 7

Lecture 162 Cost function 8

Lecture 163 Cost function 9

Lecture 164 Cost function 10

Lecture 165 Cost function 11

Section 7: Feedback Linearization Controller

Lecture 166 Equations of motion for position control (inertial frame) - exercise

Lecture 167 Equations of motion for position control (inertial frame) - solution

Lecture 168 General feedback control architecture

Lecture 169 Feedback Linearization Controller schematics - Part 1

Lecture 170 Differential Equations - intro

Lecture 171 Differential Equations & the control law

Lecture 172 Solving differential equations - real roots 1

Lecture 173 Solving differential equations - real roots 2

Lecture 174 Solving differential equations - real roots 3

Lecture 175 Solving differential equations - complex roots 1

Lecture 176 Solving differential equations - complex roots 2

Lecture 177 Solving differential equations - complex roots 3

Lecture 178 Solving differential equations - complex roots 4

Lecture 179 Using the exponent for controlling a system - exercise

Lecture 180 Using the exponent for controlling a system - solution

Lecture 181 Poles & Laplace domain

Lecture 182 From poles to differential equation constants - exercise

Lecture 183 From poles to differential equation constants - solution

Lecture 184 From differential equations to state-space representation

Lecture 185 Eigenvalues in control engineering & Determinants

Lecture 186 Computing eigenvectors

Lecture 187 Laplace VS Fourier frequency domain

Lecture 188 Moving poles

Lecture 189 Feedback Linearization Controller schematics - Part 2

Lecture 190 Simulation results with real & complex poles 1

Lecture 191 Simulation results with real & complex poles 2

Lecture 192 Simulation results with real & complex poles 3

Lecture 193 Feedback Linearization Controller schematics - Part 3

Lecture 194 Final Stretch - computing the final control inputs - Part 1

Lecture 195 Final Stretch - computing the final control inputs - Part 2

Lecture 196 Recommended reading: Great article about Kalman Filters

Section 8: The simulation code explanation

Lecture 197 Intro to (Linux & macOS Terminal) & (Windows Command Prompt)

Lecture 198 Python installation instructions

Lecture 199 Python installation instructions - Windows 10

Lecture 200 Python installation instructions - Ubuntu

Lecture 201 Python installation instructions - macOS

Lecture 202 Simulation analysis & code explanation 1

Lecture 203 Simulation analysis & code explanation 2

Lecture 204 Simulation analysis & code explanation 3

Lecture 205 Simulation analysis & code explanation 4

Lecture 206 Simulation analysis & code explanation 5

Lecture 207 Simulation analysis & code explanation 6

Lecture 208 Simulation analysis & code explanation 7

Lecture 209 Simulation analysis & code explanation 8

Lecture 210 Simulation analysis & code explanation 9

Lecture 211 Simulation analysis & code explanation 10

Lecture 212 Simulation analysis & code explanation 11

Lecture 213 Simulation analysis & code explanation 12

Lecture 214 Simulation analysis & code explanation 13

Lecture 215 Simulation analysis & code explanation 14

Lecture 216 Simulation analysis & code explanation 15

Lecture 217 Basic intro to Python animations tools

Lecture 218 Simulation codes & course summary document

Section 9: Extra: MPC constraints applied to the UAV

Lecture 219 Recap of MPC constraints in autonomous cars 1

Lecture 220 Recap of MPC constraints in autonomous cars 2

Lecture 221 Recap of MPC constraints in autonomous cars 3

Lecture 222 Recap of MPC constraints in autonomous cars 4

Lecture 223 Recap of MPC constraints in autonomous cars 5

Lecture 224 Application of MPC constraints to UAV drone 1

Lecture 225 Application of MPC constraints to UAV drone 2

Lecture 226 Application of MPC constraints to UAV drone 3

Lecture 227 Application of MPC constraints to UAV drone 4

Lecture 228 Application of MPC constraints to UAV drone 5

Lecture 229 No solution example (autonomous cars) 1

Lecture 230 No solution example (autonomous cars) 2

Lecture 231 Installation of solver libraries - Intro

Lecture 232 Installation of solver libraries - Windows 10

Lecture 233 Installation of solver libraries - Ubuntu

Lecture 234 Installation of solver libraries - MacOS

Lecture 235 UAV drone Python files WITH MPC constraints

Lecture 236 MPC constraints for UAV drone - code explanation 1

Lecture 237 MPC constraints for UAV drone - code explanation 2

Lecture 238 MPC constraints for UAV drone - code explanation 3

Lecture 239 MPC constraints for UAV drone - analysis of simulation results

Section 10: Last Words

Lecture 240 Thank You!

Lecture 241 Well done! You've done it! But don't stop here! Keep going forward!

Science and Engineering students,Working Scientists and Engineers,Control Engineering enthusiasts

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