Drone Modeling, Simulation & Control using Matlab/Simulink

Name: Drone Modeling, Simulation & Control using Matlab/Simulink
Rating: 4.5 (55 reviews)

Master Quadrotor Dynamics, 12-State Modeling, and PID Control Systems in Simulink.

Created byAymene Bacha

Last updated 6/2026

English

What you'll learn

Understand the fundamentals of drone dynamics including the body frame, earth frame, and the relationship between thrust, torque, and motion.
Develop the complete 12-state nonlinear mathematical model of a quadrotor drone from first principles using Newton-Euler equations.
Simulate and analyze the drone's dynamics and the performance of the closed-loop control system within a professional software environment (MATLAB/Simulink)
Design and implement PID control systems for both the attitude (roll, pitch, yaw) and altitude (vertical position) of the drone.

Course content

6 sections • 17 lectures • 2h 1m total length

Welcome & Course Orientation — what you’ll build (Quadrotor project overview)2:01
Explore drone modeling, simulation, and control with Matlab/Simulink, covering earth and body frames, Euler angles, rotation matrices, and PID-driven quadrotor dynamics.
Installing MATLAB & Simulink (legally) + UI tour & starter project8:11
Install and set up matlab and simulink with a guided tour of matlab online and desktop, sign in to mathworks, and start a trial and starter project for drone modeling.

The key concept: DoF & reference frames (Earth vs. Body) with visuals7:51
Explain the drone's six degrees of freedom and how translations and rotations occur on the x, y, z axes, using pitch, roll, and yaw, within earth and body frames.
The math behind rotations: Euler angles & rotation matrices7:58
Learn the math of drone rotations with Euler angles phi, theta, psi and the rotation matrix to transform vectors between body and earth frames, enabling Matlab/Simulink modeling and simulation.
Rigid-body Dynamics: Newton–Euler equations3:12
Explore rigid-body dynamics of a drone with Newton-Euler equations, deriving translational motion from Newton's second law and rotational motion from Euler's equation, using frames, rotation matrices, and Euler angles.
Check your fundamentals — frames, Euler angles, notation

Translational dynamics — deriving linear accelerations3:37
Derive translational motion equations for a drone using Newton's second law, combining gravity and thrust transformed by the rotation matrix into the earth frame to obtain x, y, z accelerations.
Rotational dynamics — deriving angular accelerations5:22
Derive angular accelerations using Euler's equations and model torques from propellers and gyroscopic effects. Implement cross products and Matlab/Simulink simulations to solve the drone's rotational dynamics.
Assembling the full model — 12-state vector, 4 inputs, outputs; state-space form5:18
Assemble the drone model in state-space form with a 12-state vector of x, y, z, phi, theta, psi and their velocities, plus four control inputs and outputs for Matlab/Simulink simulation.
Quadrotor modeling checkpoint

Hands-on: Simulink project structure, libraries & reusable blocks11:03
Explore the Simulink interface, compare Matlab and Simulink, and build dynamic models with drag-and-drop blocks, scope, and transfer functions, then view results in Matlab workspace.
Build the Rotational Dynamics subsystem17:40
Build the rotational dynamics subsystem of a drone in Matlab/Simulink by deriving omega from propeller torques and implementing Euler's equations with integrators and Matlab function blocks.
Build the Translational Dynamics subsystem8:07
Model the drone translational dynamics in Simulink by building integrator-based x, y, z subsystems with thrust input and gravity, validating outputs and preparing for PID control.

PID control demystified — what are P, I, D?7:36
Explore how pid control combines proportional, integral, and derivative actions to minimize error and steady-state error while damping oscillations in a Simulink transfer function model.
Hands-on in Simulink: Attitude Controller (φ, θ, ψ)4:35
Hands-on in Simulink: Position Controller (x, y, z)14:45
Design and implement PID controllers for drone x, y, z in Simulink, derive phi and theta from the inputs, and generate a 3D trajectory using a MATLAB function.
Desired references for φ, θ4:20
Explain how to derive the desired roll and pitch (phi and theta) from the x and y control inputs, using trig manipulation, arcsin relations, and small-angle intuition with Desmos.

Full course recap — what you built, validation, and next steps8:03
Recap the drone modeling workflow: derive rotation matrix from frames and Euler angles, apply Newton-Euler dynamics, build Matlab/Simulink simulations, and plan future work on friction, noise, faults, and advanced controllers.
Final lecture — Thank you1:32
Conclude the drone modeling, simulation, and control course with Matlab/Simulink, revisiting core concepts and delivering a final thanks to learners.

Requirements

Fundamental understanding of Calculus, Linear Algebra.

Description

Master the essential engineering principles behind modern autonomous drones! This complete course on Drone Modeling, Simulation, and Control is designed for engineers, students, and advanced hobbyists seeking deep, practical knowledge in robotics and UAV systems.

Move beyond theory and learn how to design a functional flight controller from the ground up. You will begin by establishing the mathematical foundation, learning to derive the full 12-state nonlinear equations of motion for a quadrotor using Newton-Euler formalism. This modeling step is critical for accurate control system development.

The course then focuses on creating a stable, high-performance flight system. You will master the design and tuning of industry-standard PID control systems for both the drone's attitude (roll, pitch, yaw) and altitude. Learn how these controllers are cascaded to ensure stable flight and precise navigation.

Crucially, all theoretical concepts are immediately translated into practice. You will use MATLAB/Simulink to build a comprehensive, closed-loop simulation model, allowing you to test, analyze, and validate your control designs against real-world disturbances.

Key skills you will gain:

Deriving the complete 12-state dynamics model.
Implementing and tuning PID controllers for all 6 degrees of freedom.
Simulating complex dynamics in a professional environment.
Developing the expertise required for advanced control techniques.

Enroll today to transform your understanding of drones into a professional-grade engineering skill!

Who this course is for:

Engineering Students (Electrical, Mechanical, Aerospace, and Computer Engineering) looking for a comprehensive capstone project or deeper practical knowledge in dynamics and control.

Drone Modeling, Simulation & Control using Matlab/Simulink

What you'll learn

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Course content

Kickoff & Environment Setup2 lectures • 10min

Theory Foundations: Physics of Drone Motion3 lectures • 19min

Mathematical Modeling of a Quadcopter3 lectures • 14min

From Theory to a Working Simulink Model3 lectures • 37min

Design the PID controler4 lectures • 31min

Review & Wrap-Up2 lectures • 10min

Requirements

Description

Who this course is for: