Course Unit Page

Academic Year 2022/2023

Learning outcomes

At the end of the course, the student is knows how to create a mathematical model of an unmanned aircraft and he knows how to design and implements the main attitude and trajectory control systems. Through experimental lab activities, he also gets familiar with the fundamentals of Remote-Piloted Aircraft Systems flight planning and operations.

Course contents

  • Unmanned Systems: history and evolution.
  • GNC systems design: the Model-Based approach.
  • Fixed-wing and conventional helicopter platforms: performance analysis and optimization. Optimal design configurations.
  • Multirotor platforms: performance analysis and optimization. The aerodynamics of propellers conceived for multirotor professional applications. Optimal design configurations. 
  • The making of a simulation model: Blade-Element Theory analysis and implementation of multirotor equations of motion in Matlab/Simulink environment. 
  • Open-loop analysis of 6dof multirotor dynamic model.
  • Basic inner-loop controllers for attitude and vertical speed stabilization of multirotor platforms.
  • Guidance, Navigation, and Control system definition and implementation. Application examples and test cases.


  • Brian L. Stevens, Frank L. Lewis, Eric N. Johnson, Aircraft Control and Simulation, Third Edition, John Wiley & Sons, Inc., 2016.
  • Peter D. Talbot, et al., A mathematical model of a single main rotor helicopter for piloted simulation, NASA Technical Memorandum (TM) 84281, NASA, 1982. 
  • Kimon P. Valavanis (Editor), Unmanned Aircraft Systems - The Current State-of-the-Art, Springer, 2013.
  • J. Gordon Leishman, Principles of Helicopter Aerodynamics, Second Edition, Cambridge Aerospace Series, Cambridge University Press, 2006.
  • Gareth D. Padfield, Helicopter Flight Dynamics: The Theory and Application of Flying Qualities and Simulation Modelling, Second Edition, Blackwell Publishing, 2007.
  • Donald McLean, Automatic Flight Control Systems, Prentice Hall, 1990.

Teaching methods

  • Class lectures on digital whiteboard.
  • Collaborative computer programming in Matlab/Simulink environment.
  • Numerical exercises and simulations.
  • Educational visits and laboratory experiences.

Assessment methods

The final exam consists in an oral test. During the oral discussion, each student will have to answer theoretical questions and show the results of a personal simulation project carried out during the course. The teacher will assess the student's preparation and his mastery on the provided results. 

Teaching tools

  • Digital whiteboard.
  • MathWorks products for computer programming.
  • Laboratory equipments.
  • Unmanned electric platforms.

Office hours

See the website of Emanuele Luigi De Angelis