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NPTEL

Drone Systems and Control

NPTEL via Swayam

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Overview

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ABOUT THE COURSE:This course provides a comprehensive introduction to drone technology, encompassing fundamentalaerodynamics, simulation, sensor integration, control systems, and autonomous navigation. Beginning withdrone types and safety regulations, the course progresses through 6-DoF modeling in Simulink, sensorcalibration and Kalman filtering in MATLAB, and classical autopilot design. Advanced topics include pathplanning algorithms (RRT, A*), obstacle avoidance techniques (potential fields, collision cones, controlbarrier functions), trajectory tracking, and an introduction to simultaneous localization and mapping(SLAM) and Sim2Real transfer using ROS and AirSim.INTENDED AUDIENCE:Undergraduate and graduate students.Drone industries.Anyone interested in drone navigation, guidance,and control.PREREQUISITES: Familiarity with mathematics subjects such as linearalgebra, differential equation, etc.Proficiency in Simulink/MatLabINDUSTRY SUPPORT: Any drone company

Syllabus

Week 1:
  • 1. Types of drones, Fundamentals of aerodynamics – lift, thrustand drag, safety requirements, regulations, applications
  • 2. Rigid body transformation/rotation
  • 3. Dynamic model of multi-rotor
Week 2:
  • 4. Sensors: IMU (gyro, accelerometer), GPS, altitude sensors,vision-based sensors
  • 5. Basics of estimation and Kalman filtering
  • 6. Kalman filtering
Week 3:
  • 7. Extended Kalman filtering
  • 8. Introduction to control system, Laplace Transforms
Week 4:
  • 9. Control system: Transient response
  • 10. Control system: Frequency response
Week 5:
  • 11. Control system: Stability
  • 12. Proportional-integral-derivative controller design
Week 6:
  • 13. Classical auto-pilot design: auto-takeoff and landing
  • 14. Classical auto-pilot design: auto-stabilization
  • 15. Classical auto-pilot design: attitude hold and position hold
Week 7:
  • 16. Basics of PX4, MatLab-motors-PX4 interfacing
  • 17. Design of real-time implementation in flight controller
  • 18. Experiment: Attitude bench controller tuning
Week 8:
  • 19. Scenario generation, point-to-point navigation
  • 20. Global path planning: basic algorithms
Week 9:
  • 21. RRT algorithm
  • 22. A* algorithm
Week 10:
  • 23. Obstacle avoidance, Artificial potential field
  • 24. Collision cone-based approaches
  • 25. Control barrier function
Week 11:
  • 26. Trajectory tracking: PID controller
  • 27. Trajectory tracking: Model predictive control
  • 28. Design implementation of sense-and-avoid for multi-rotoraerial vehicle
Week 12:
  • 29. Introduction to mapping, SLAM, Visual SLAM, visualperception, object detection
  • 30. Sim2Real: ROS, AirSim

Taught by

Prof. Suresh Sundaram

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