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Udemy - Applied Control Systems for Engineers 2 - UAV drone control
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + .srt | Duration: 207 lectures (22h 46m) | Size: 7.54 GB
Modelling + 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 for Engineers 1 - autonomous vehicle
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, their applications 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.
In this course, you will receive a full package when it comes to learning about how to 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, but you will also be exposed to some of the most powerful control techniques out there such as Model Predictive Control and Feedback linearization.
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!
Who this course is for
Science and Engineering students
Working Scientists and Engineers
Control Engineering enthusiasts
Homepage
https://www.udemy.com/course/applied-control-systems-for-engineers-2-uav-drone-control
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