Modeling and Control of Aerospace Vehicles
Objectives
In this curricular unit the students will have a broad perspective of the various types of aerospace vehicles and the main modeling, analysis, and control methods used for these vehicles, understanding their potential but also their limitations. Simultaneously, the students will develop know-how and experience of designing and implementing concrete control strategies for this type of vehicle.
To this end, the intended learning outcomes for this curricular unit are the following:
OA1. Revise classical control systems analysis and design tools;
OA2. Formulate control system models for simple aerial and space vechicle;
OA3. Analyze and design control systems using a state-space techniques;
OA4. Analyze and design control systems using a MIMO frequency domain techniques;
OA5. Develop solutions to concrete control systems problems in aerospace.
General characterization
Code
13141
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 but not an absolute requirement.
Bibliography
Recommended:
- Theoretical-practical presentations, Bruno Guerreiro, UNL, 2023.
- S. Skogestad and I. Postlethwaite. Multivariable Feedback Control: Analysis and Design, 2nd Edition, John Wiley & Sons, 2005. https://folk.ntnu.no/skoge/book/
- A. Tewari. Advanced control of aircraft, spacecraft and rockets. John Wiley & Sons, 2011.
Additional:
- Exercise Book, Bruno Guerreiro, 2023.
- Project assignment, Bruno Guerreiro, 2023.
- 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
- J. P. Hespanha, Linear Systems, 2nd Ed., Princeton University Press, 2018.
- J. M. Lemos, Controlo no Espaço de Estados, IST Press, 2019.
- MATLAB Primer: https://www.mathworks.com/help/pdf_doc/matlab/getstart.pdfng.pdf
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 may use a Blended Learning (B-Learning) methodology, where new contents are breafly introduced asynchronously using Moodle, while the synchronous classes are used to consolidate the contents, addressing students'' questions, and solving more complex problems. The use of active learning techniques will also be encouraged.
Evaluation method
The final grade (F) is defined as: F = 0.5*T + 0.15*Q + 0.35*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, and as such, there is the final exam as an alternative. The Project assignments (P) will count as the laboratorial assessment grade.
Subject matter
M1. Introduction and revisions
M1.1. Motivation, applications, and classification of aerospace vehicles
M1.2. Classical control system analysis and design
M2. Modeling of Aerospace Vehicles
M2.1. State-space formulation of dynamic systems
M2.2. Reference frames and rigid body dynamics and kinematics
M2.3. Examples of aerospace vehicles and models
M3. Time-domain analysis and synthesis
M3.1. Equilibrium points, linearization, and phase plots
M3.2. Discrete state-space models and discretization methods
M3.3. Controllability, observability, stability and performance
M3.4. Cascaded PIDs and LQR control design
M4. MIMO frequency domain analysis and synthesis
M4.1. Transfer function matrices and SVD modal decomposition
M4.2. MIMO frequency response analysis and synthesis
M4.3. Nominal and robust analysis and synthesis