Applied Control Systems 4: UAV: Quaternions + MPC + PID

Master UAV attitude control using quaternions, PID, and MPC with Python implementations and gimbal lock avoidance.

What you’ll learn
Learn the fundamentals of quaternion math in representing an object’s attitude in 3D
Learn to mathematically model, differentiate, and integrate quaternion-based systems
Learn about Euler angle limitations due to Gimbal Lock and its avoidance using quaternions
Apply quaternion representation in MPC and PID algorithms in UAV attitude control for tracking trajectories
Implement quaternion based MPC and PID controllers in Python in offline simulation
Learn to convert Euler angles to quaternions and the challenges associated with it

Requirements
Applied Control Systems 1: autonomous cars: Math + PID + MPC
Applied Control Systems 2: autonomous cars (360 tracking)
Applied Control Systems 3: UAV drone (3D Dynamics & control)
Basic skills in Python programming

Description
This course provides a comprehensive introduction to using quaternions for UAV (unmanned aerial vehicle) attitude representation and control, with a practical focus on implementing PID and Model Predictive Control (MPC) algorithms in Python. Ideal for engineers, robotics enthusiasts, and control systems students, this course demystifies quaternions, a powerful mathematical tool that resolves issues like Gimbal lock encountered with traditional Euler angles.You will begin with fundamental concepts of quaternions, learning their algebra, derivatives, and integration techniques crucial for accurately modeling 3D rotational dynamics. The course emphasizes quaternion-based state representation and the benefits it offers in maintaining smooth and singularity-free attitude descriptions.Building on this foundation, you will explore how to formulate and apply PID controllers on quaternion attitude errors to stabilize UAV orientations effectively. Furthermore, advanced control design using MPC will be covered, demonstrating how to incorporate quaternions into predictive control frameworks for enhanced performance in UAV attitude control.All control algorithms and quaternion mathematics are implemented step-by-step in Python, with practical coding examples and guidance on simulation and real-time applications. This hands-on approach ensures you gain both theoretical understanding and practical programming skills.Key highlights include:Comprehensive study of quaternions vs Euler angles and how to avoid Gimbal lockMathematical modeling and numerical integration of quaternion-based systemsPID control design tailored for quaternion attitude errorsModel Predictive Control formulation with quaternions for UAVsPython coding tutorials for control algorithms and simulationsApplications specifically focused on UAV attitude control systemsBy the end of the course, students will be equipped to confidently model, simulate, and control UAV attitude dynamics using quaternions and advanced control methods, making it a perfect fit for professionals and students working in robotics, aerospace, and autonomous systems development.This course is perfect for those interested in UAV control, robotics, aerospace engineering, nonlinear control, and Python programming for control systems.

Who this course is for
Control Systems engineers
Aerospace and Mechanical engineers

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