Unmanned Aerial Vehicles
Objectives
In this curricular unit the students will have a broad perspective of the modeling, estimation, and control methods used in unmanned aerial vehicles (UAVs), understanding their potential but also their limitations. Simultaneously, the students will have a realistic experience of design and implementation of control strategies for UAVs.
To this end, the intended learning outcomes for this curricular unit are the following:
OA1. Analyze and model unmanned aircraft;
OA2. Understand how to use sensor fusion techniques for UAVs;
OA3. Understand and design linear and nonlinear controllers for UAVs;
OA4. Understand and design planning algorithms for UAVS;
OA5. Develop concrete control and estimation solutions for UAVs.
General characterization
Code
13147
Credits
6.0
Responsible teacher
Bruno João Nogueira Guerreiro
Hours
Weekly - 4
Total - 56
Teaching language
Português
Prerequisites
Students should have a strong foundation in Linear Algebra and Mathematical Analysis, which are typical for most engineering students. Taking introductory courses in signals, systems, and in control systems is recommended.
Bibliography
- UAVs Course Slides, Bruno Guerreiro, NOVA-FCT, 2024.
- S. Leutenegger, C. Huerzeler, A. K. Stowers, K. Alexis, M. Achtelik, D. Lentink, P. Oh, and R. Siegwart. Flying Robots, in Handbook of Robotics. Springer, pp. 623–670, 2016. https://doi.org/10.1007/978-3-319-32552-1_26
- R. Beard, and T. W. McLain, "Small Unmanned Aircraft: Theory and Practice", Princeton University Press, 2012. http://press.princeton.edu/titles/9632.html
Additional:
- K. Åström and Richard M. Murray. Feedback systems: An Introduction for Scientists and Engineers, 2nd Ed., Princeton university press, 2021. URL: https://press.princeton.edu/books/hardcover/9780691193984/feedback-systems
- H. Khalil, Nonlinear Systems, 3rd Edition, Pearson, 2002. https://press.princeton.edu/books/hardcover/9780691193984/feedback-systems
- J. P. Hespanha, Linear Systems, 2nd Ed., Princeton University Press, 2018.
- J. M. Lemos, Controlo no Espaço de Estados, IST Press, 2019.
Teaching method
The course is organized in theoretical-practical classes and practical classes. In the theoretical-practical classes the concepts are introduced and applied in concrete cases from an analytical point of view. In addition, the practical (or laboratory) classes are directed to work on further analytical problems tipical of the TP class topics under study, as well as to the development and implementation of techniques applied to concrete cases, with the goal to obtain experimental results and their analysis.
The course uses a Blended Learning (B-Learning) methodology, where new contents and their consolidation takes place both on the TP classes and asynchronously on Moodle, leaving more time in TP classes to consolidate topics, address questions, and solving complex problems. The use of active learning techniques is also encouraged.
Evaluation method
The final grade (F) is defined as: F = 0.45*T + 0.1*Q + 0.45*P
- Tests (T): the theoretical-practical component will be primarily evaluated through 2 tests.
- Online quizzes (Q): Moodle short-quizzes and other online assessment tools.
- Project (P): project assignment to promote a deeper understanding, applied to a concrete scenario.
The assessment components Tests (T) and Online Quizzes (Q) are considered the theoretical-practical component, for which there is the final exam as an alternative. The Project assignments (P) will count as the laboratorial assessment grade.
Both T and P components have a minimum grade of 9.5 points in 20.
Subject matter
M1. Introduction and modeling of UAVs:
M1.1. Motivation, classification and components of UAVs.
M1.2. Revisions on rigid body dynamics and state-space models.
M1.3. Models of fixed-wing, rotary-wing and hybrid UAVs.
M2. Filtering and sensor fusion:
M2.1. Sensors and state estimation with Kalman filter.
M2.2. Complementary filter.
M2.3. Extended Kalman filter.
M3. Motion control:
M3.1. Linear and optimal control techniques.
M3.2. Lyapunov stability analysis.
M3.3. Non-linear control techniques.
M3.4. Trajectory tracking and geometric attitude control.
M4. Planning and supervision:
M4.1. Trajectory and path planning.
M4.2. Planning with obstacles.